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Research Collection
Doctoral Thesis
Factors affecting vocal learning performance In juvenilesongbirds
Author(s): Lee, Juneseung
Publication Date: 2020-02
Permanent Link: https://doi.org/10.3929/ethz-b-000408636
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DISS. ETH NO. 26437
Factors affecting vocal learning performance In juvenile songbirds
A thesis submitted to attain the degree of DOCTOR OF
SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich)
presented by
JUNESEUNG LEE
Dr. sc. ETH Zurich
born on 05.02.1986
citizen of Republic of Korea
accepted on the recommendation of
Prof. Dr. Richard Hahnloser Prof. Dr. Satoshi Kojima
Prof. Dr. Ryosuke Tachibana
2020
Factors affecting vocal learning performance in juvenile songbirds
PhD Thesis Juneseung Lee- Intitute of Neuroinformatics Uni / ETH Zurich
2020
Supervisor: Prof. Dr. Richard Hahnloser
Co-Supervisor: Prof. Dr. Satoshi Kojima
Co-Supervisor: Prof. Dr. Ryosuke Tachibana
Abstract
Children develop language through listening and imitating vocal sounds from parents or other adult members. Similarly, songbirds, like humans, gradually acquire acoustically complex but stereotyped imitations of vocalizations produced by conspecifics. Therefore, the songbird is the ideal model to understand the underlying mechanism for vocal learning, and its study outcome can be compared with human speech learning. The songbird brain contains a premotor area HVC which is functionally equivalent to Broca’s area in humans. HVC plays an important role in song production and perception. In both humans and songbirds, it is often found that some are good learners and others are not. It is not clear that this variability is due to diverse practical learning efforts, or just a congenital talent, or even individual differences in neural development in premotor brain areas. Our goal is to study latent mechanisms of vocal learning processes, both on the behavioral and neural levels.
One idea is that songbirds become better singers if they intensely train their vocal skills from a young age. However, we found out that in songbirds, deliberate practice does not always correlate well with the corresponding change in the imitation accuracy of their song models. In the first part of this PhD thesis, we show that the manner in which songbirds modulate acoustic variability may explain the subsequent change in performance better than the amount of practice. Using the zebra finch as our animal model, we analyze the relationship between daily vocal practice (duration of putative singing) in juveniles and the change in acoustic similarity with their tutors’ songs. We found that there is little to no correlation between the two.
In a second part of the Thesis, we dive into in-depth neural level discovery beyond the behavioral factors for vocal learning. We hypothesize that during the vocal learning period in juvenile zebra finches there is a difference in neural activity in motor cortical areas between good and bad vocal learners. To test this hypothesis, we need to longitudinally record neural activity in HVC during the sensory and the sensory-motor period. Firstly, we show that longitudinal neural recordings can be performed in 45 dph young birds using both single- and two-photon microscopy. Then we preview the vocal learning performance of juveniles that underwent longitudinal calcium imaging. We show that some of the juveniles were still able to learn from tutoring performed during calcium imaging. In this way, we could lay a cornerstone to develop a method to understand the entire song learning process at the population level with near single-cell or single-cell resolution.
Zusammenfassung
Kinder entwickeln Sprache durch Zuhören und Imitieren von Stimmgeräuschen von Eltern oder anderen erwachsenen Mitgliedern. Ebenso erwerben Singvögel wie Menschen nach und nach akustisch komplexe, aber stereotype Imitationen von Lautäußerungen, die von Artgenossen produziert werden. Daher ist der Singvogel das ideale Modell, um den zugrundeliegenden Mechanismus für das Vokallernen zu verstehen, und sein Studienergebnis kann für das Erlernen menschlicher Sprache geteilt werden. Songbird hat eine Prämotorfläche HVC, die funktionell der Fläche von Broca beim Menschen entspricht. HVC spielt eine wichtige Rolle bei der Produktion und Wahrnehmung von Songs. Bei beiden Arten wird häufig festgestellt, dass einige gute Lernende sind und andere nicht. Es ist nicht klar, dass dies auf den unterschiedlichen praktischen Lernaufwand oder nur auf ein angeborenes Talent oder sogar auf individuelle Unterschiede in der neuralen Entwicklung im Bereich des Vormotors zurückzuführen ist. Wir möchten den verborgenen Mechanismus sowohl des Lernprozesses im Verhalten als auch des neuronalen Prozesses beim vokalen Lernen kennen. Einige glauben, dass sie bessere Sänger hätten sein können, wenn sie ihre stimmlichen Fähigkeiten absichtlich trainiert hätten, seit sie jung waren. Wir haben jedoch herausgefunden, dass bei Singvögeln bewusstes Üben nicht immer gut mit der entsprechenden Änderung der Imitationsgenauigkeit ihrer Songmodelle korreliert. Im ersten Teil dieser Doktorarbeit haben wir gezeigt, dass die Art und Weise, wie Singvögel die akustische Variabilität modulieren, die Änderung der Leistung besser erklären kann als die Menge an Übung. Anhand des Zebrafinken als Tiermodell analysieren wir den Zusammenhang zwischen der täglichen Stimmpraxis (Dauer des mutmaßlichen Gesangs) bei Jugendlichen und der Veränderung der akustischen Ähnlichkeit mit den Liedern ihrer Tutoren. Wir fanden heraus, dass es zwischen den beiden kaum eine bis gar keine Korrelation gibt. In einem weiteren Bereich beschäftigen wir uns mit der eingehenden Entdeckung der neuronalen Ebene, die über die Verhaltensfaktoren für das vokale Lernen hinausgeht. Wir gehen davon aus, dass es in motorisch-kortikalen Bereichen einen Unterschied in der neuronalen Aktivität zwischen guten und schlechten Stimmlernern während der Stimmlernphase beim juvenilen Zebrafinken gibt. Um die Annahme zu beweisen, müssen wir die neuronale Aktivität in der HVC während der sensorischen und sensorisch-motorischen Periode des juvenilen Zebrafinken in Längsrichtung aufzeichnen. Im zweiten Teil dieser Arbeit haben wir zum einen gezeigt, dass longitudinale neuronale Aufzeichnungen von 45dph-Jungvögeln sowohl mit einem Einzel- als auch mit einem Zweiphotonenmikroskop durchgeführt werden können. Anschließend sehen wir uns die stimmliche Lernleistung von Jugendlichen mit Längsschnitt-Calciumbildern an. Wir haben gezeigt, dass einige der Jugendlichen immer noch in der Lage sind, aus dem während der Kalziumbildgebung durchgeführten Nachhilfeunterricht zu lernen. Auf diese Weise könnten wir den Grundstein für die Entwicklung einer Methode legen, mit der der gesamte Song-Lernprozess auf Bevölkerungsebene mit einer Auflösung von nahezu einer Zelle oder einer Zelle verstanden werden kann.
Acknowledgement
I would like to express my sincere gratitude to Prof. Richard Hahnloser for his dedicated grounding, immense knowledge and continuous support during my doctoral course. His guidance helped me in every way of research and writing of this thesis.I would also like to thank his support of the SNF grant 31003A-156976. My doctoral course would not have completed without it. Besides my advisor, I would also like to give thanks to the rest of my thesis committee: Prof. Satoshi Kojima and Prof. Ryosuke Tachibana, for their encouragement. Even at hardship, your insights and suggestions had been the most helpful to expand my research in various perspectives. My sincere thanks also go to Dr. Gagan Narula as my closest colleague who contributed so much on my work. I would also like to thank Dr. Joshua Herbst for providing the data for analysis. In addition, I will always remember my beloved colleagues at the songbird-group, Heiko Hörster, Ziqian Hwang, Sophie Cave-Lopez, Daniel Düring, Corinna Lorenz, Diana Rodriguez, Homare Yamahachi, and Anja Zai. Last but not least, a special thanks to my family. Words cannot express how grateful I am to my parents and my wife for their endless love and support. Thank you so much for believing in me.
Table of Contents
Factors affecting vocal learning performance in juvenile songbirds .......................................... 2
1. Introduction ........................................................................................................................... 8
1.1. About Songbirds and Vocal learning ....................................................................... 10 1.1.1. The Zebra Finch - taeniopygia guttata ............................................................................. 10 1.1.2. Birdsong - Learned Vocalizations .................................................................................... 12 1.1.3. A Songbirds Brain .............................................................................................................. 15 1.1.4. The Cortical Premotor Area HVC .................................................................................... 19
2. Song imitation performance in juvenile songbirds is uncorrelated with amount of practice ................................................................................................................................ 26
2.1. Method ................................................................................................................... 27 2.1.1. Experimental strategy .......................................................................................................... 27 2.1.2. Tutoring ............................................................................................................................... 27 2.1.3. Song recordings ................................................................................................................... 28 2.1.4. Song Density ........................................................................................................................ 29 2.1.5. Quantification of song development .................................................................................. 29
2.2. Results ..................................................................................................................... 31
2.3. Discussion ............................................................................................................. 40
3. Neural dynamics during vocal learning in juvenile ......................................................... 41
3.1. Introduction ............................................................................................................. 41
3.1.1. Functional Single- and Two-Photon Imaging in Neuroscience ........................ 41 3.1.2. Single- and Two-Photon Microscopy ................................................................................ 43 3.1.3. Fundamentals of Calcium Imaging ................................................................................... 46 3.1.4. Calcium Imaging in the Zebra Finch ................................................................................ 53
3.2. Methods ................................................................................................................... 54 3.2.1. Virus injection ..................................................................................................................... 54 3.2.2. Tracer injection ................................................................................................................... 56 3.2.3. Head plate and cranial window implantation .................................................................. 56 3.2.4. Miniscope implantation in juvenile zebra finch ............................................................... 57 3.2.5. Calcium imaging ................................................................................................................. 58 3.2.6. Sound recording and tutor song presentation .................................................................. 58
3.3. Data analysis ............................................................................................................ 59
3.3.1. Song similarity .................................................................................................................... 59 3.3.2. Single and two photon imaging .......................................................................................... 59 3.3.3. Statistical analysis for identified neurons. ........................................................................ 60
3.4. Results ..................................................................................................................... 61 3.4.1. Longitudinal quality control of cranial window ............................................................... 61 3.4.2. Longitudinal neural imaging ............................................................................................. 62 3.4.3. Tutoring during head-fixation ........................................................................................... 64
3.5. Discussion ................................................................................................................ 65
4. Genetically encoded calcium indicators evaluated in the zebra finch ....................................... 67
4.1. Introduction .................................................................................................................. 67
4.2. Method .......................................................................................................................... 67
4.3. Results .......................................................................................................................... 68
4.4. Discussion .................................................................................................................... 69
5. Welfare of zebra finches during two-photon imaging is investigated ...................................... 70
5.1. Introduction ............................................................................................................. 70
5.2. Method ................................................................................................................... 71 5.2.1. Preparations for safe Head-fixation ........................................................................................ 71 5.2.2. Lighting in two-photon chamber without damaging to photon detector ............................. 71 5.2.3. Optimizing imaging duration for the well-being of subject .................................................. 72 5.2.4. Daily condition check by body mass and no. of vocalization measuring ............................. 73
5.3. Discussion ............................................................................................................. 74
Appendix A. ......................................................................................................................... 76
Bibliography ........................................................................................................................ 79
1. Introduction
One of the guiding principles for learning complex motor skills is captured by the idiom “practice makes perfect”. Watson (Watson, 1929) believed that practice is the major driving force behind success in skillful activities such as sports and music. Later, the influential work of Ericsson and colleagues (Ericsson et al., 1993b) established that deliberate practice, i.e., intense rehearsal of domain specific activity, explains much of the difference between the performance levels of athletes. This and related work (Allen & Barnsley, 1993) led to Malcolm Gladwell’s famous “10,000 hour” rule (Gladwell, 2009) which posits that a person needs to rehearse an activity for at least 10,000 hours to become an expert. However, recent meta-analysis has shown that the amount of deliberate practice accounts for only 20% of the variance in performance in a wide variety of motor skills, and even in cognitively demanding tasks related to education (Gobet & Campitelli, 2007; Hambrick, Altmann, Oswald, Meinz, & Gobet, 2014; Macnamara, Hambrick, & Oswald, 2014). The share of variance explained by practice is even lower (~1%) when only elite athletes are studied (Macnamara, Moreau, & Hambrick, 2016). Furthermore, Macnamara et al. (2016) provide evidence for the influence on performance by factors such as the complexity of a game environment, whether it is a team/individual sport, ball vs non-ball sport, etc. These co-variates explain a significant share of the variance in performance than practice alone.
The relationship between practice and performance of a complex, natural motor behavior is of particular significance in young, maturing subjects for two reasons. First, animals need to devote vital time and energy to practice even though the fruit of their labor might be earned in the future. Second, practice should lead to robust (stable) learning that generalizes to changing environments. To ensure robustness and strong generalization, an animal must explore many behavioral variants, which requires even more rehearsal. More concretely, if practice is considered a form of policy search for motor control (Peshkin, Kim, Meuleau, & Kaelbling, 2000; J. Peters & Schaal, 2008; Shute, Graf, & Hansen, 2006), practice will yield better policies. Such notions are well motivated by the mathematical formulation of reinforcement learning, which is considered a prime candidate for the acquisition of skills through practice (Ericsson, 2009; Ericsson et al., 1993a; Helton, 2005; Kulasegaram, Grierson, & Norman, 2013) in animals and in robots (policy search survey(Deisenroth, Neumann, & Peters, 2013) Jober, Peters, Diesenroth). For example, Policy gradient RL ((J. Peters & Schaal, 2008; Sutton & Barto, 1998; Williams, 1992) is an algorithm that adapts the policy parameters to maximize
expected reward, and to compute a low-variance estimate of expected reward, the agent must perform as many rehearsals as possible. The role of practice in animals is far from resolved because of the scarcity of densely sampled data on motor learning. We inspect the relationship between practice and performance in passerine birds, in which we have gathered longitudinal auditory recordings. Songbirds such as the zebra finch (Taeniopygia guttata) belong to a small group of species that are vocal learners, i.e. they acquire their species-specific acoustic vocabulary through sensory experience and motor practice after birth. Vocal learning begins with relatively incoherent motor “babbling”, and slowly matures into spectro-temporally stereotyped acoustic sequences as the bird ages. The developmental process in songbirds mirrors that of human speech acquisition, although on a much shorter time scale (Allison J. Doupe & Kuhl, 1999; Menyhart, Kolodny, Goldstein, DeVoogd, & Edelman, 2015; Mooney, 2014). Using the zebra finch as our animal model, we analyze the relationship between daily vocal practice (duration of putative singing) in juveniles and the change in acoustic similarity with their tutors’ songs. We find that there is little to no correlation between the two. To go beyond behavioral comprehension, recordings of coordinated activity of neuronal populations in motor cortical areas are required during vocal development (Leonardo & Fee, 2005; Lynch, Okubo, Hanuschkin, Hahnloser, & Fee, 2016; Okubo, Mackevicius, Payne, Lynch, & Fee, 2015a; Picardo et al., 2016a; Simonyan & Horwitz, 2011). Recent studies during vocal learning ( Kosche et al., 2015; Okubo et al., 2015; Vallentin et al., 2015; Roberts et al., 2010) indicate that HVC’s neuronal dynamics is modulated by the tutor song. Unfortunately, neural activity in HVC has been characterized in various animals at distinct phases in different times, ranging from a few minutes to a few days. Since song learning takes at least 60 days, the previous studies lack the period required to understand the whole process. In the second part of this thesis, we established fundamental prerequisites to determine how the neuronal subpopulations (e.g. HVC neurons projecting to other nuclei) in HVC are modulated during the entire learning period. Ultimately, we want to understand the neural modulation by auditory inputs, such as tutor song, and its neural correlation to the bird’s own song development. In the introduction, we will layout the general knowledge on songbird and its vocal learning scheme, especially in the zebra finch. Then discuss why this is the suitable
model to study the important factors affecting to vocal learning. This PhD thesis contributes to discovering critical factors related to vocal development in both behavioral and neural system.
1.1. About Songbirds and Vocal learning
’Songbird (also called Oscine, from Latin oscen)’ is the common name of a bird belonging to the kindom Passeri of the perching birds (Passeriformes). According to Scott and Harshman, there are more than 5000 species found all over the world (Scott and Harshman, 2013). The vocal organ, syrinx, of songbirds share a common architecture and uniquely developed to produce an elaborate and diverse bird song. Among the well-known three distantly related avian vocal learning groups – songbirds, parrots, and hummingbirds, ‘birdsong’ exclusively refers to the term of the vocal output of songbirds. In many aspects, the process of the birdsong learning of songbirds is analogous to the process of the language learning of human beings. According to the reviews from Jarvis in 2012, vocal pathways in songbird’s brain seem to have analogous pathways implicated in human speech learning and production (Jarvis, 2012). As it is yet far way to understand human speech learning, studies in songbird brain has been implicated important neural mechanisms underlying vocal learning and production.
1.1.1. The Zebra Finch - taeniopygia guttata
Zebra finches are found natively in Austrailia (castanotis) and the East Timor and the Sunda Islands of Indonesia (guttata) (Immelmann, 1965b). They reside in almost entire continent of Australia except south and north coastal regions to avoid cool moist atmosphere. Nowadays, one can easily find them in the wildlife of the American continent or Portugal due to the human introduction (Birdlife international 2013). As they are highly social birds, wild zebra finches use to congregate a group of a few hundred individuals. Throughout their entire life, they maintain a monogamous pair relationship (Zann, 1994).
Figure 1.1.: Zebra finch family in our colony. Photograph taken by Heiko Hörster.
Zebra finches in captivity can breed all year long when sufficient water is provided and it attempts to bear young several times in each breeding season. Nottebohm showed the Juvenile zebra finches can successfully imitate the given relatively short tutor song exposure (40 playbacks of 30 seconds long) during their sensitive vocal learning period (O Tchernichovski, Lints, Mitra, & Nottebohm, 1999). Due to these reasons, zebra finches are greeted among neuroscientists studying vocal learning. Desmond Morris primarily suggested that zebra finches could be studied as an ideal behavioral model in the lab in the mid 20th century (Morris, 1954). Even though Desmond Morris’ interest in zebra finches were mainly behavioral aspects of breeding and courtship, shortly zebra finches were recognized as the most prominent animal model for neurobiology of birdsong research. Over last many decades, profound investing-ation on zebra finch brain has been performed and brought deep understanding of vocal learning and production from neural pathways to neural network level. As the vocal learning of zebra finch is analogous to the speech learning of humans, understanding of the neural mechanism underlying birdsong learning would contribute to the human’s language learning in the end (Brainard & Doupe, 2013).
1.1.2. Birdsong - Learned Vocalizations
Vocal learning is a special type of motor learning shared with only few other kingdoms of animals (humans, cetaceans, bats, pinnipeds, elephants, and three bird groups – songbirds, parrots, and hummingbirds). Songbirds are distinctive vocal learners which produce complex vocalizations by learning from conspecifics. They use their singing not only to draw sexual attentions but also to defend and mark their territory (Immelmann, p. 1965a). Some songbirds sing even sing complex duets but as for zebra finches, only males produce complex learned songs and calls even though female zebra finches produce short innate calls with a wide range (Blair Simpson & Vicario, 1990). Zebra finches sing one unique and stereotyped song throughout their entire life except learning period in juvenile. Typically, their song begins with 3-5 introductory notes and a variable number of stereotyped song motif follows the notes. A song motif consists of 3-7 different syllables in robustly fixed order with 5-15 ms intermittent gap (or silence). Each syllable is about 100-300 ms in duration and sometimes 5-50 ms sub-syllabic structures can be accompanied with it (see figure 1.2).
Figure 1.2.: Spectrogram of the song of r15s12. The color coded (from low to high power, blue to red) spectrogram shows power spectrum at different frequencies over time. This one full song bout begins with 4 introductory notes and is followed by 3-4 syllables. The 3rd rendition’s 4th syllable is truncated.
Juvenile male zebra finches can learn their song from a template provided by a conspecific male tutor bird. In general, song learning can be divided into two phases (Tamura, 1964): a sensory period during which juvenile zebra finches
acquire a model of tutor song template and a sensorimotor period within which they refine unstructured and plastic subsong by modifying the acquired song through practice and auditory feedback. Through the two overlapping sensory and sensorimotor periods (see figure 1.3), young adult zebra finches crystallize the song at about 90 dph (Konishi, 1965). Auditory input critically affects to the result of vocal output for juvenile zebra finches. In case of failure to hear a template song from a tutor bird at both the sensory and sensorimotor periods (typically 15-60 dph), young bird will produce abnormal and unstructured vocal output (Tamura, 1964). Konish, in 1965, found out that loss of hearing ability during practical duration can also result the same abnormal vocal output even if the young bird acquired a template from a tutor bird. In both cases, nonetheless, its vocal output becomes more structured and stereotyped, even though it has higher plasticity than the other birds which got a template and a time to practice, after sensorimotor duration ends.
Figure 1.3.: Song learning in zebra finches undergoes in two overlapping
periods: a sensory period within which a template song from a tutor
male is acquired and a sensorimotor period during which vocal output
is more refined to match the chosen tutor template. Normally juvenile
male zebra finches start to produce very unstructured vocal output
(also called subsong) at around 25 dph. Then juveniles incorporate
more and more elements from the tutor song but their vocal output
stays highly variable from each song bout to the next one. At the end
phase of the sensorimotor period, song structure is well structured and
stereotyped and this is called crystallization. Under normal conditions
in nature, juvenile produces a faithful copy of the template tutor song.
If a juvenile zebra finch is exposed to a tutor song during the sensory period, it does
Sensory
Sensorimotor
Crystallized
Dph
0 35 60 90 15
not have to hear the template tutor song again to produce a devoted copy during practicing duration. The important factor for the faithful reproduction of the tutor song is that hearing during a sensory period for juvenile can be reproduced for the life time (Tamura, 1964). These features substantiate the theory that a process for template song memory storage during the sensory period is independent of vocal practice. Vocal learning for juvenile songbirds is a highly precise yet rather convenient to characterize motor learning process that copies from conspecific birds. It is a straightforward example of template storage and its matching. Template matching here notes to the process of comparing the memory previously registered in sensory coordinates to the produced output sound in motor coordinates. This process needs a coordinate transformation but its mechanism has not been well understood yet. Songbird researchers anticipate understanding of vocal learning in songbird will give a clue to the extensive comprehension of the transformation mechanism in the sensory and motor coordinates. In further extent, the vocal learning process is analogous to human language learning. Indeed, researchers studying vocal learning suggest a set of important parallel principles in the two phenomena – human speech and birdsong: First, both birdsong and human language are learned vocalizations that serve specific communication of each species. Second, the vocalizations of birds and humans are structured by an interaction of experience and predisposition. Third, the best learning period for vocalization is both at a young age. Fourth, in both species vocal practice is followed by a duration of auditory ‘priming’. In human infants sensory priming is conducted by a loss of ability to discriminate sounds from all languages. After sensory priming, roughly 12 months later, human babies are only able to discern between sounds they formerly learned to categorize differently. Fifth, social interaction is a critical factor to improve the learning performance in both species. Sixth, despite the fact that cortex of songbirds and mammals are differently structured, both species share distinct sets of telencephalic regions with a similar organization for vocal perception and production. The parallels even expand to the genetic level. FoxP2 is known as a gene that is not only involved in human speech related disorders but also vocal processing in songbirds (Heston & White, 2015). Thanks to the above factors, songbirds are highlighted as a translational animal model for vocal learning to humans (Allison J. Doupe & Kuhl, 1999). In the next section, we have a close look at the brain structures and vocal pathways that provide the detail for song learning and production in songbirds.
1.1.3. A Songbirds Brain Mammals and songbirds are vertebrates sharing a common anatomy of the central
nervous system. However, telecephalon of songbirds differs from telecephalon of
mammalians in the components of the pallium. The songbird’s pallium is mainly
organized as unit of nuclear while the mammalian pallium is layered cortex form
(Jarvis, 2004). Yet the basal ganglia in both families are nuclear organizations as
part of the telencephalon.
Figure 1.4.: Schematic sagittal view of a zebra finch brain. It describes
three major song related pathways: the auditory pathway (AP, green), and the motor pathway (MP, red), and the anterior forebrain pathway (AFP, blue).
Song learning and production are traditionally proclaimed to involve three major
song pathways in the brain, see Figure 1.4. We briefly introduce them here:
The auditory pathway (AP) is specified by the cochlea, neural responses to auditory
input, and by afferent neural connections originated from the auditory sensory
organs. Since many regions of all three main pathways cross over the above
criteria, the auditory pathway is normally limited to the starting of the auditory
input stream, disregarding regions with known additional functionality. Moreover,
neurons of the auditory pathway show responses to a wide range of auditory inputs.
Other telencephalic areas typically respond only to very specific auditory inputs.
Nucleus Ovoidalis (Ov) is the major auditory region of the thalamus (Karten,
1967). Field L is the main telencephalic region projected by the Ov (Vates,
DLM
LMAN
NCM Field L
Nif
CM
Uva
Ov
Broome, Mello, & Nottebohm, 1996). According to the research by Fortune and
Margoliash in 1992, Field L can be splited into 4 cytoarchitectonically defined
subdivisions: L1, L2a/L2b, and L3. The majority of Ov projections end in L2a and
L2b (Adret et al., 2012). However, Ov is a bit more complex structure itself, and it
is known to consist of ‘Ov core’, ‘Ovm’ and ‘Ov shell’ in more detail. Researchers
found out that Ov core projects L2a, Ovm projects to L2b, and Ov shell projects to
caudal medial nidopallium (NCM), L1 and L3. NCM and the field L subdivisions
are most often connected in corresponding ways, as described in Figure 1.5. NCM
has corresponding connection with Caudal mesopallium (CM) projecting to HVC
and Nif. By far, this is known as a complete representation of all connections
possibly delivering auditory information. Here we do realize that auditory
information is distributed expansively over a large area of songbird telencephalon.
Indeed, auditory stimulation induces neural responses in the areas of the auditory
pathway as well as the areas of the anterior forebrain pathway (A. J. Doupe &
Konishi, 1991; Allison J. Doupe, 1997) and in premotor areas (Katz & Gurney,
1981; McCasland & Konishi, 1981).
Figure 1.5.: Auditory input to the telencephalon regions in songbirds.
The caudal medial nidopallium and Field L are targeted by
auditory information originated from the thalamic nucleus Ov “core” (Ovoidalis). Figure taken from (Vates et al., 1996).
In higher order area such Nif, the neural response for the birds own song (BOS) shows surprisingly higher preference over any other auditory stimulus and it is still unexplained phenomenon ((Bauer et al., 2008). On the other hand, neurons in CM and Field L were reported to convey motor related signals during singing. For example, they fired according to an anticipated vocal output or even to the contrast between the predicted and the actual output (G. B. Keller & Hahnloser, 2009). Overall, the gathered data stresses the close interconnection of motor control and auditory processing in the songbird telecephalon.
Areas related to the motor pathway (MP) are essential for song production. In adult
songbirds, lesion in any of the regions along the MP results in alteration or even
loss of song output. Production of song moreover is required to be convoyed by
premotor neuron’s activity time-locked to song. With no doubt, a direct neural
connection must exit between the muscles of the syrinx and an area of the motor
pathway.
Nottebohm et al. showed that hypoglossal neurons of nXIIts nucleus innervate to
syringeal muscles (Nottebohm, Stokes, & Leonard, 1976). They further tracked
neuron projections from nXIIts to the correspondent to primary motor cortex in
mammals, the robust nucleus of the arcopallium (RA) in songbird. It is known that
RA innervation of nXIIts is topographic (Blair Simpson & Vicario, 1990). RA
neurons fire a sequence of short and sparse bursts of APs time-locked to a song
motif during singing ((Dave, Yu, & Margoliash, 1998).
Major input to RA is rendered by the lateral magnocellular nucleus of the anterior
nidopallium (LMAN) and premotor area HVC (used as a proper name)
(Nottebohm, Paton, & Kelley, 1982). Nottebohm et al. and Bottier et al. also
revealed that only HVC and RA are compulsory for adult song production but not
LMAN by lesioning RA and its afferent regions (Bottier, 1984) (Nottebohm et al.,
1976). Much like in RA, Neural activity in HVC is highly associated with song
structure but considerably sparser in HVCRA (RA projecting neurons from HVC)
neurons than in RA neurons. Only one high frequency AP burst is fired by HVCRA
neurons per motif (Richard H R Hahnloser, Kozhevnikov, & Fee, 2002;
Kozhevnikov & Fee, 2007). Participation in the singing related regions afferent to
HVC is more modulatory and less understood.
HVC has direct innervation from the interface nucleus of the nidopallium (Nif)
(Nottebohm et al., 1982). While irreversible Nif pharmacological lesions do not
significantly impair the production of songs (Cardin & Schmidt, 2004), it has lately
been shown that reversible pharmacological inactivation leads to a reduction in
song stereotypes in the brief term (Naie & Hahnloser, 2011). While the bird is
singing, Nif neural activity increases (McCasland, 1987).
The uvaeformis (Uva) thalamic nucleus projects directly and indirectly (via Nif) to
HVC using two separate neuron projection classes (Akutagawa & Konishi, 2010;
Nottebohm et al., 1982; C. Z. H. Wang, Herbst, Keller, & Hahnloser, 2008).
Williams and Vicaro reported that singing related Uva activity discovered in
chronic multi-unit recordings and modified song structure followed by electrolytic
Uva lesions (Williams H, 1993).
However, it is not fully understood about a critical participation in song motor
control of subsidiary HVC-afferent areas or afferent to any other motor pathway
areas.
The anterior forebrain pathway (AFP) is a basal ganglia (BG)-thalamocortical loop
bridging the cortical LMAN, the dorsal lateral nucleus of the medial thalamus
(DLM), and Area X, which is homologue to BG of the mammalian). An input
signal is provided to AFP by HVCX (Area X projecting neurons from HVC)
neurons (Nottebohm et al., 1976). Nottebohm et al also showed neurons from
LMAN are projecting to the RA and sending AFP output (Nottebohm et al., 1982).
In consistency with BG-thalamocortical architecture in mammalian circuits that
were known to be involved in motor learning (Middleton & Strick, 2000) (JB,
2006), the AFP plays a significant role in song learning. An early research
disclosed that pre- or during song learning lesions of the entire magnocellular
nucleus of the nidopallium (MAN) have serious impacts on song learning and
production while lesioning MAN in adult birds leaves song development intact
(Bottjer & Arnold, 1984). Subsequently, Nordeen and Nordeen report that slow
song deterioration is led by deafening in adult birds (Nordeen & Nordeen, 1992).
However, they also showed that LMAN lesion can partially prevent the
deterioration (Nordeen & Nordeen, 2010). By far, several studies indicate that AFP
is accountable for creating exploratory variability and is therefore required for song
learning (Andalman & Fee, 2009; Kao, Doupe, & Brainard, 2005; Ölveczky,
Andalman, & Fee, 2005).
A set of HVC neurons that projecting to Area X provide input from MP to the AFP.
Therefore, HVC is thought to play a key role, i.e. by conveying motor information
to the AFP. By now, HVC has been most intensively studied area in songbird brain.
In the next section, we will take a closer look at HVC.
1.1.4. The Cortical Premotor Area HVC As seen in Figure 1.4, HVC is superficially located on the posterior pallium. Hence,
it has been an easy subject for songbird researchers and deeply studied in the past.
Even nowadays, most recent imaging researches still focus on HVC because it is
the sole brain area involved in singing which can be reachable by optical methods
until 2009 when the GRIN lens was highlighted as a deeper brain region imaging
method (Barretto, Messerschmidt, & Schnitzer, 2009). However, the GRIN lens
requires removal of tissues in the pathway from the brain surface to the target
region. Therefore, HVC is the least affected brain area for optical imaging. This
particular advantageous characteristic suggests accessing the functional microscopy
techniques to deal with questions concerning HVC, including on motor control and
on auditory processing.
In this section, we will take a closer look at HVC to set the foundation for the
questions we want to examine utilizing in vivo single- and two-photon calcium
imaging.
HVC connectivity
The anatomy of songbird's brain itself provides a clue about the significance of
HVC for singing. First, HVC has a direct pathway to the syrinx muscles through
nXIIts (see fig. 1.4). Second, HVC receives auditory inputs from various and
diverse neural pathways. And lastly, HVC is originally a source of input to a basal
ganglia-thalamocortical loop which also provides input to the motor pathway.
This is a rough description of HVCs connectivity. Here are some more in-depth
fact about HVC: input from the medial magnocellular nucleus of the anterior
nidopallium (MMAN) is received in HVC (Nottebohm et al., 1982), is reciprocally
linked with RA (Roberts et al., 2008) as well with a subsection of CM, termed
nucleus Avalanche (Av) (Akutagawa & Konishi, 2010; Bauer et al., 2008;
Nottebohm et al., 1982).
The projections to regions efferent to HVC derive from diverse populations of
HVC projection neurons. We describe neurons with an established projection target
with subscript letters specifying the target. For example, HVC neurons projecting
to Area X, we describe this as HVCX neurons. Recently discovered set of HVCAv
neurons is estimated to contain a few hundred neurons (Akutagawa & Konishi,
2010) and its element is not well known.
HVCRA and HVCX neurons, however, are relatively largely proportioned and well-
studied. Another major set of neurons with no projections outside of HVC was
introduced firstly through electrophysiology in slices (Dutar et al., 1998). They are
so called HVC interneurons or HVCI neurons.
HVC physiology
Three essential discoveries initiated intensive research of the neural basis of song
learning and of singing in songbirds: First was the necessity of the gain of auditory
memory of kin song (Tamura, 1964). The second was the necessity of acoustic
feedback during song production in juvenile period (Konishi, 1965). And third, it
has been discovered that a particular set of interconnected forebrain areas are
compulsory for song production (Nottebohm et al., 1976). Notably, early
electrophysiological studies tried to discover areas of auditory processing and
acoustic memories in songbird brains. Auditory responses to the introduction of
noise and tone bursts in HVC neurons were first reported by Katz and Gurney (Katz
& Gurney, 1981), but they did not find which stimuli were favorable. Shortly after
the first discovery, McCasland and Konishi found that even after the birds were
deafened, their HVC showed signals of singing associated activities that were time-
locked to a song. Additionally, McCasland and Konishi discovered that HVC
neurons respond more favorably to the BOS compared to the BOS played in reverse
(rBOS). They also showed that activity patterns in HVC have not altered by
auditory stimuli (including BOS) played back when the bird was singing. It
appeared that auditory input to HVC while the bird was singing to be prohibited,
which allowed a pure premotor function to HVC (McCasland & Konishi, 1981).
Margoliash's additional research confirmed the general preference for the BOS of
HVC neurons over a broad range of stimuli: 1) temporally and spectrally altered
BOS, and 2) conspecific songs (CON). Feedbacks of HVC neurons showed high
responsiveness to temporal and spectral features of the BOS (Margoliash, 1983),
and also to the temporal sequence of individual syllables (Margoliash & Fortune,
1992).
It seems the downstream structured muscles connecting from the RAs to the
syringeal to be weakly myotropic (Blair Simpson & Vicario, 1990). There is no
hard proof for a topographical link, neither efferent nor afferent, in the case of HVC
(Foster, Mehta, & Bottjer, 1997). Neither did multi-unit feedbacks to acoustic
stimuli unfold the topographic structuring of HVC activity. On the other hand, BOS
stimulation seems to induce similar responses within the whole HVC (Sutter &
Margoliash, 1994).
Auditory responses of neurons in HVC are nevertheless of astounding nature:
single-unit responses to acoustic stimulation with the BOS can be caused by
impressive temporal precision, showing a few bursts of APs, and precisely time-
locked to the song by millisecond precision (Huetz, Del Negro, Lebas, Tarroux, &
Edeline, 2006; Lewicki & Konishi, 1995; Mooney, 2000). Mooney explained the
BOS responses of the three major types of HVCs neurons are different by showing
the subthreshold mechanisms in anesthetized zebra finches. When HVCRA neurons
are depolarizing during the entire song, HVCX neurons experience extended
hyperpolarization. Even so, both neuron types express bursts that are time-locked
within millisecond precision to the song playback (Mooney, 2000). HVCI neurons
react with continuously increasing firing rate during the whole BOS stimulus.
Since anatomy provides different probabilities, speculation about the pathway on
which acoustic information extend to HVC aroused. Irreversible excitotoxic
lesioning (Cardin & Schmidt, 2004) of Nif and reversible inactivation (Coleman,
Roy, Wild, & Mooney, 2007) steers to loss of acoustic feedback in HVC.
Electrophysiology showed that NifHVC neurons are particularly selective for the
BOS similar to HVC neurons. Nonetheless, the sparse responses of HVC projection
neurons to BOS arises solely within HVC. Nif responses are more retained and
closely follow subthreshold HVC projection neuron responses (Coleman et al.,
2007).
Figure 1.6.: Different types of HVC neuron responses selectively time-locked to BOS stimulation. Both HVCX and HVCRA projection neurons fire a small number of short bursts of AP, precisely time-locked to the song. However, while HVCRA neurons undergo strong depolarization during BOS playback, HVCX neuron responses exhibit sustained depolarization. On the other hand, HVCI neurons show increased firing rate during BOS playback. All neuron types are BOS specific. Figure adapted from (Mooney, 2000).
The HVC afferent thalamic region Uva has been revealed to gate BOS responses
in HVC because electrical stimulation of Uva throughout BOS playback suppresses
HVC BOS reactions, directly through HVC inhibition and indirectly through Nif
input inhibition to HVC. Reversible Uva lesions do not critically influence HVC
activity, neither voluntary nor in response to auditory input (Coleman et al., 2007).
Figure 1.7.: HVC activity in a bird that is singing and freely moving. On the top,
there is a spectrogram of a motif of the bird's song. Below displays raster plots of
various identified HVC neuron types. Horizontal lines break up individual neurons.
Every song variation can be seen on a new line. Throughout the song motif, HVCRA
neurons only fire on a strongly precise burst of APs, HVCX neurons display several
brief bursts of APs, and HVCI neurons express more continuous but loosely song-
locked firing. The figure above is taken from (Kozhevnikov & Fee, 2007).
CM is also involved in auditory input to HVC. Bauer et al. (Bauer et al., 2008)
reported that auditory responses in HVC and Nif suppressed by CM inactivation.
Contrary to the above-mentioned studies, HVC auditory responses continued in
this study even after irreversible Nif lesions. This indicates direct and immediate
auditory input from CM to Nif.
To sum up, HVC auditory responses are affected by several interconnected afferent
regions with direct projections to HVC. It seems there is no direct feedforward
auditory pathway connected to HVC. Still, the remarkable characteristic of BOS
selectivity of CM and Nif seems to emerge. CM is a bit BOS selective (Bauer et
al., 2008) and Nif strongly BOS selective (Coleman et al., 2007). Nif is also highly
selective for HVC projection neurons that respond with sparse and time-locked
bursts of APs to a song. Therefore, there is a hierarchy in feature detection that
culminates in the sparse depiction of sensory features in HVC, despite the lack of
apparent hierarchical connectivity (Blättler & Hahnloser, 2011).
HVC premotor activity corresponds to the sparsity explained in the BOS
stimulation responses and demonstrates the remarkable temporal precision of
repeated song production in birds: Hahnloser et al. antidromically identified
HVCRA neurons and recorded activity in freely behaving zebra finches. The
downstream motor pathway is innervated by these neurons. Intriguingly, their
premotor code is temporary; because each HVCRA neurons only fires a brief burst
of APs once per each song motif (Richard H R Hahnloser et al., 2002; M. A. Long,
Jin, & Fee, 2010), see Figure 1.7. Experiment results in which HVC has been
cooled down further support the idea that HVC modulates the temporal precision
firing to the bird's song. Cooling down HVC led in a song being dilated at all time
scales (M. a Long & Fee, 2009), indicating a linear temporal code. This, however,
opposes with the latest study suggesting that HVC could encode the production of
vocal gestures (Amador, Stathopulos, Enomoto, & Ikura, 2013). Although this is
an intriguing concept, this study depends heavily on the fit of a complicated
physical model of song production and requires further research and support.
Figure 1.8.: Levels in spatial organization are schematically described in nervous systems. A whole nervous system of a sizeable vertebrate species is able to extend over several meters whereas the smallest functional elements are single molecules such as functioning ion channels. In between of them, we find systems, maps, networks, neurons, and synapses. Multi-photon microscopy empowers the study of neural phenomena in the extend of neurons to maps and now reaches to Synapses. Figure adapted from (Sejnowski, 1988).
When HVCRA neurons were selectively degenerated by neuron-type-specific
method, the bird showed song deterioration and this confirms their important role
as the primary HVC premotor output. On the contrary, singing is not impaired by
the targeted ablation of HVCX neurons (Scharff, 2000). In addition, HVCX neurons
also show sparse firing during singing, but several times per song motif unlike
HVCRA (Kozhevnikov & Fee, 2007), see Figure 1.7. HVCx neurons are assumed
to have a role in song learning since they project to the AFP. An intriguing research
by Prather et al. (Prather, Peters, Nowicki, & Mooney, 2008) offers a little more
light on their function: HVCRA neurons do not show responses to auditory stimuli
in freely behaving awake swamp sparrows, a songbird with a simple repertoire of
song types; only HVCX neurons respond. Generally, HVCX neurons respond only
to one version of the song repertoire, so called 'primary song'. The response to the
song is precisely time-locked. These neurons simultaneously fire within the
primary song while singing. Singing associated activity is unchanged by distorted
auditory input and consequently 'corollary discharge'. Responses are also elicited
by playing comparable songs from conspecific birds. This research defines HVCX
neurons as mirror neurons, much the same as mirror neurons in monkey's premotor
cortex (Rizzolatti, Fadiga, Gallese, & Fogassi, 1996). Mirror neurons are
considered important for learning imitations (Fabbri-Destro & Rizzolatti, 2008).
Idea that HVC is engaged in song learning suggests that structural modification
occur during learning period within HVC. Roberts et al. observed that dendritic
spines of spinous HVC neurons sustain and grow in size after the first exposure of
a juvenile bird to the tutor's song1. In HVC projection neurons rapid spine turnover
was identified in juvenile birds that were never introduced to a tutor song and ended
about 60 days post-hatch, the age where the sensory stage is believed to end roughly
in zebra finches, or after exposure to tutor song.
HVC neurons were virally infected with Green Fluorescent Protein (GFP) in the
last-mentioned research and chronically imaged with a two-photon microscope. So
far, a survey of neural activity in songbirds using two-photon microscopy has not
been recorded. We present two-photon microscopy in the next chapter and how it
allows neural activity research.
2. Song imitation performance in juvenile
songbirds is uncorrelated with amount of practice
1 Both HVC projection neurons (HVCRA and HVCX) have spiny dendritic
arborizations, whereas HVCI neurons are aspiny (Mooney, 2000).
2.1. Method
2.1.1. Experimental strategy
When juvenile zebra finches are at 15 days post hatch (dph), we separated adult
males from the juveniles. This is because juvenile males typically enter a sensory
song-learning period at this age (Immelmann, 1969). Then only their mothers in a
soundproof recording chamber raised them until 41 dph for group 1 (n=17 birds)
and 46-47 dph for group 2 (n=4 birds). This had the effect of isolating the juveniles
from adult song (only male zebra finches sing). All birds (n=21 birds) did not
undergo any surgery.
At approximately 42-47 dph, the song-isolate juveniles were exposed to a singing
adult male tutor. Every morning for approximately 90 minutes, tutors were placed
in the recording chamber in a separated cage adjacent to the juveniles. Each
juvenile was alone in the chamber for the remainder of the day. This allowed us to
acquire high-quality recordings when juveniles develop their songs. Therefore,
juvenile males were exposed to only their tutors’ songs and their songs throughout
the experiment. All vocal production inside the recording chamber were recorded
and monitored.
2.1.2. Tutoring For tutoring, we exposed a different adult male tutor to each juvenile. We
minimized differences between the tutor songs by using only successfully tutored
tutors by the same song playback in their cage where each song playback was
triggered by a button. Juveniles were tutored with only live tutors instead of
tutoring by song playback. This is because birds that learn from song playback
alone showed low percentage of learning rate (see also Derégnaucourt et al., 2012).
In zebra finches, it is sufficient to make fairly complete imitation throughout giving
a total daily duration of 30 seconds playback containing 40 tutor songs per day.
(Peters et al., 1992) (Tchernichovski et al., 1999). Typically, tutors in each tutoring
session produced hundreds of song motifs in our experiment. This means that the
90 minutes exposure time to tutor was enough for achieving good imitation. Each
juvenile was exposed to only one tutor across three weeks for group 1 and two
weeks for group 2. Because song separation from multiple birds is not an easy task,
especially when the song of juvenile starts to imitate the song of tutor, we only
analyzed vocal productions of juvenile in isolation without the tutor.
2.1.3. Song recordings We recorded the vocalizations of juveniles throughout song development. Using
customized Matlab base software, captured signal by a wall microphone was band-
pass filtered between 100 Hz and 10 kHz. Then the filtered signal is digitized at a
sampling rate of 32 kHz with 16-bit precision. In case of continuous recording by
the software would result in 230 MB of data and it contains long recording time of
non-song signals, redundant for our song analysis. Thus, we decided to record
songs selectively and maximize the recording time of song only signals. We
developed a method for identifying zebra finch vocalizations based on harmonic
sound structure and separating them from other gratuitous sounds such as wing
flaps. At every integer multiple of the fundamental frequency, harmonic sounds are
characterized by signal intensities. The spectral sound density ɸ(ω) of the sound
waveform was calculated as a function of sound frequency ω in 16-ms windows.
The harmonic power h is defined at a given frequency ω by the product of multiples
and spectral densities at the frequency ω thereof:
ℎ(𝜔) =&ɸ(i ∗ ω).!
"#$
We usually chose a total of N = 7 density multiples. Eventually, we defined the
harmonic level as
𝐻 =𝑚𝑎𝑥%ℎ(ω)
𝑚𝑖𝑛%!&'ℎ(ω()
which is higher for sounds with a harmonic structure than for broadband sounds of the same overall intensity. 𝐻(𝑡) was calculated as a function of the window center t discretized in 4 ms steps. Whenever 𝐻(𝑡) was above a given threshold value during more than 50% of a 0.6 s time period, a save event was triggered. Then the recorded sounds were streamed to a file on a hard disk. Each recorded file started one second before the trigger event and ended after no trigger event was seen for an entire second (the criterion for song recording was evaluated every 4 ms). On a typical day we obtain about two hours of song recording data separated into 1500 files. Recorded files typically
contained bouts of vocalizations but individual calls or noises such as wing flaps were rarely found. Cage noise was unusually recorded only when intermingled with vocalizations. Our method based on the harmonic level performed superior song selection than with other methods based on sound amplitude alone (i.e. threshold triggered song selection of the root-mean-square (RMS) of the sound waveform in 16 ms windows). We inspected the histograms of harmonic detector levels for each day of recording (in days 50 – 60 post hatch) for all birds. Based on a visual inspection of the histograms, we estimate that ~5% of all vocalizations recorded during a day were rejected by our choice of threshold (50% above in a 0.6 s period).
2.1.4. Song Density The amount of singing was computed by summing the durations of all song
production from juvenile male in each day. A song is defined as a series of at least
3 consecutive syllables and automatically detected (each syllable is detected when
it is at least 10 ms long with a gap of at most 500 ms to other syllables in juveniles).
Including 32 ms short margins added before the first and after the last syllable, the
total song duration was defined as the interval exceeding 800 ms from the onset of
the first to the offset of the last syllable. Superthreshold intervals of sound
amplitude is defined as a root mean square sound waveform filtered between 500
Hz and 4 kHz and syllable is detected where the sound amplitude above the given
threshold intervals.
2.1.5. Quantification of song development
To quantitatively measure and compare song development for each bird with tutor,
we used a freely distributed Matlab based software package called Sound Analysis
Pro (SAP). This software provides a tool for calculating the similarity between the
tutor song and juvenile’s song (Tchernichovski et al., 2000), and has been
implemented in many song analysis process and widely utilized in the songbird
community (Benichov et al., 2016; Okubo, Mackevicius, Payne, Lynch, & Fee,
2015b; Pearre, Perkins, Markowitz, & Gardner, 2017). To estimate the similarity
proportion of sounds in two different vocalizations, corresponded features such as
frequency modulation, pitch, Wiener entropy, amplitude modulation, syllable
duration, and spectral continuity are taken into account. In our analysis, we used
the default parameter settings provided with SAP.
On the level of song motifs of the juvenile’s song and tutor song, song similarity
values are computed with SAP. As SAP strongly depends on relative duration of
motifs (typically similar length of motifs results larger similarity values), we
restricted our similarity analysis to relatively matched duration of the juvenile’s
song and tutor song motifs. In the next section, we describe a selection method to
extract the most typical and representative motifs from variable juvenile songs.
To calculate song similarity on each analyzed day, we extracted the ten most
representative (most typical) tutor song motifs. Using SAP, we calculated all 100
similarity values between the 10 x 10 pairings of tutor and juvenile motifs. The
similarity values were small during the early plastic song phase and increased
gradually from 50% at the beginning of tutoring to 80% on the last day of tutoring.
The standard deviation 𝜎 of song similarity values 𝑥" was calculated for the N=10
x 10 = 100 daily parings:
𝜎 = 5$!∑ (𝑥" − �̅�)!"#$ eq.(1)
where �̅� is a mean of the N similarity values. Also, the mean 𝜎9and its standard
deviation 𝜎) was calculated for n = 267 days (across 14/18 birds from experiment
start until 60 dph):
𝜎9 = ∑ 𝜎"*"#$ eq.(2-1)
𝜎) = 𝜎:1 − +*,$
∙ =𝚪(n/2)𝚪(𝑛−12 )
>+
eq.(2-2)
where 𝚪 is the gamma function.
For 4/18 birds, where we were not able to perform sufficiently reliable song
selection because of large noise in the song recording, we manually inspected a
random selection of 100 putative song motifs and selected the 10 best putative
motifs for analysis.
We performed song similarity analyses starting about six days after tutoring onset, roughly when young birds produced rhythmic sequences of precursor syllables (Liu, Gardner, & Nottebohm, 2004; Okubo et al., 2015a). Note that our analysis avoided the sub-song phase (recordings before tutoring) during which we were
unable to select song motifs. This is because there is no validated method to perform song analysis at such a young age.
2.2. Results
We exposed n = 18 juvenile male zebra finches each to the song of one adult male
(n=6 tutors) zebra finch. At 15 days post hatch (dph), the juveniles were separated
from adult male birds and placed in the care of their mothers. At around 30 dph,
male juveniles from each clutch (family of juveniles + mother) were moved to
individual acoustic isolation chambers. Starting at 45 dph (+/- 4 day), the pupils
were exposed for 90 mins each day to adult male birds placed in a separate cage in
their chamber, Fig 1A. Tutoring continued for three weeks in 14/18 birds and for
two weeks in 4 birds due to logistical constraints. All vocalizations with a minimum
amount of harmonic content (see Methods, Harmonics detection) were recorded
and classified as either directed singing (the tutor was present), or undirected
singing (the juvenile was alone). For practical reasons, juvenile birds were
continually recorded for a variable number of days from a minimum of 64 days (2
birds) to a maximum of 95 days post hatch (2 birds).
We segmented any sound elements from background noises by thresholding sound
amplitudes (root mean square of filtered microphone signal). We separated
syllables from noise (wing flap etc.) using a semi-supervised clustering approach
(see Methods). In 14/18 birds, we semi-automatically clustered song syllables into
their distinct types by backtracking from the last day of recording because older
birds produce more stereotyped song motifs, which allowed us to cluster song
syllables, calls and introductory notes using a nearest neighbor method applied to
spectrograms projected onto the top 20 principal components. After clustering a
particular day (e.g. the last day), we used the clustered syllables to cluster the
precedent day. Obtained clusters were manually corrected by visual identification
of outliers. In 4/18 birds, we did not perform clustering because of excessive
acoustic variability and recording artifacts. The total duration of putative singing
on a given day was given by the summed duration of syllables (without silent gaps)
within bouts.
a
b
c
d
Figure 2.1. Juveniles gradually improve the quality of their songs. (a)
Birds were housed in acoustic isolation from 30 days post hatch (dph) on and
exposed to their tutor for 90 minutes every day for three weeks starting (on
average) from 45 dph. We longitudinally recorded their vocalizations. (b)
Song similarity of candidate song motifs for the same bird as a function of
time since experiment start. The black line marks the three weeks tutoring
period. (c) The duration (in seconds) of putative singing produced by b13r16
as a function of time since experiment start. (d) Example log-power
spectrograms of putative song motifs produced by the juvenile b13r16 with
increasing duration of the experiment (top to bottom). For reference, two
tutor motifs are shown on the top and the bottom. These were among the
daily best candidates for the computation of song similarity, in terms of
spectral and temporal stereotypy.
We found that song production increased rapidly after experiment onset and
plateaued within two weeks at an average of 2.92 ± 0.56 x 103 s/day (average over
10-25 days since tutoring with data from a minimum of 7 birds), Fig 2a. The total
tutor song produced by the tutor in the presence of the juvenile was 1.36 ± 8.23 x
103 s/day (n=4 tutors).
a
50 60 70 80 90
Pearson r : -0.046 p : 0.75 n = 18
a b
d
30
40
50
60
70
80
% si
mila
rity
1
1.5
2
2.5
3
3.5
4
4.5
singi
ng d
urat
ion(
s)
10 4
1 2 3 4 5 6Increment in duration (s) ! 10 4
-20
-10
0
10
20
30
40
50
Incr
emen
t in
Sim
ilarit
y (%
)
50-60 dph
60-70 dph
70-80 dph
80-90 dph
n = 18
n = 18
n = 15n = 9
n = 9
days post hatch (dph)
0 1 2 3 4total tutor song (s) ! 10 4
-10
0
10
20
30
40
50
Sim
ilarit
y fro
m 5
0 to
60
dph
(%) Pearson r: -0.13 p : 0.6 , n = 18
0 10 20days post tutoring
0
1000
2000
3000
4000
5000
Sing
ing
dura
tion
(s)
c
b
c
50 60 70 80 90
Pearson r : -0.046 p : 0.75 n = 18
a b
d
30
40
50
60
70
80%
sim
ilarit
y
1
1.5
2
2.5
3
3.5
4
4.5
singi
ng d
urat
ion(
s)
10 4
1 2 3 4 5 6Increment in duration (s) ! 10 4
-20
-10
0
10
20
30
40
50
Incr
emen
t in
Sim
ilarit
y (%
)
50-60 dph
60-70 dph
70-80 dph
80-90 dph
n = 18
n = 18
n = 15n = 9
n = 9
days post hatch (dph)
0 1 2 3 4total tutor song (s) ! 10 4
-10
0
10
20
30
40
50
Sim
ilarit
y fro
m 5
0 to
60
dph
(%) Pearson r: -0.13 p : 0.6 , n = 18
0 10 20days post tutoring
0
1000
2000
3000
4000
5000
Sing
ing
dura
tion
(s)
c 50 60 70 80 90
Pearson r : -0.046 p : 0.75 n = 18
a b
d
30
40
50
60
70
80
% si
mila
rity
1
1.5
2
2.5
3
3.5
4
4.5
singi
ng d
urat
ion(
s)
10 4
1 2 3 4 5 6Increment in duration (s) ! 10 4
-20
-10
0
10
20
30
40
50
Incr
emen
t in
Sim
ilarit
y (%
)
50-60 dph
60-70 dph
70-80 dph
80-90 dph
n = 18
n = 18
n = 15n = 9
n = 9
days post hatch (dph)
0 1 2 3 4total tutor song (s) ! 10 4
-10
0
10
20
30
40
50
Sim
ilarit
y fro
m 5
0 to
60
dph
(%) Pearson r: -0.13 p : 0.6 , n = 18
0 10 20days post tutoring
0
1000
2000
3000
4000
5000
Sing
ing
dura
tion
(s)
c
d
e
0 1000 2000 3000 4000 5000 6000ï20
ï10
0
10
20
30
40
50
prev day song duration (s)
Cha
nge
in s
imila
rity
n=15 birds r: 0.045 p: 0.464
50 60 70 80 90
Pearson r : -0.046 p : 0.75 n = 18
a b
d
30
40
50
60
70
80
% si
mila
rity
1
1.5
2
2.5
3
3.5
4
4.5
singi
ng d
urat
ion(
s)
10 4
1 2 3 4 5 6Increment in duration (s) ! 10 4
-20
-10
0
10
20
30
40
50
Incr
emen
t in
Sim
ilarit
y (%
)
50-60 dph
60-70 dph
70-80 dph
80-90 dph
n = 18
n = 18
n = 15n = 9
n = 9
days post hatch (dph)
0 1 2 3 4total tutor song (s) ! 10 4
-10
0
10
20
30
40
50
Sim
ilarit
y fro
m 5
0 to
60
dph
(%) Pearson r: -0.13 p : 0.6 , n = 18
0 10 20days post tutoring
0
1000
2000
3000
4000
5000
Sing
ing
dura
tion
(s)
c
Figure 2.2. Singing duration in neither juveniles nor tutors correlates
with increments in song similarity. (a) Singing rate in juveniles continually
increases with time spent in the recording chamber and saturates within two
weeks of tutoring onset (solid line: mean, shaded area: s.e.m). (b) Average
percent similarity score (black) across ten best putative song motifs as a
function of age. Error bars represent mean ± s.e.m across birds (the number
n of birds analyzed is specified for each day). Shown are also the singing
durations (red) on the same days. Error bars represent mean ± s.e.m across
available birds. (c) Across four consecutive 10-day periods there is no
significant Pearson correlation (r = -0.046, p = 0.75) between increments in
average similarity and singing duration (N = 51 data points from n = 18 birds).
The colors indicate the juveniles’ age. (d) Across 15 birds there is no
significant Pearson correlation (r = 0.045, p = 0.464) between similarity
change on days i-1 and i+1 and singing duration on day i (with i ranging from
the start day of recording from 38-48 dph to 60 dph, depending on the bird,
N = 267 data points, n = 15 birds). The tutoring start day was within 4 (± 5)
days from the start day of recording. (e) There is no significant Pearson
correlation (r = -0.13, p = 0.6) between the increment in average similarity
from days 50 to 60 and the total tutor song exposure (n = 18 birds).
Across development, juveniles gradually improved the quality and stereotypy of
their song imitations. An example bird’s putative song motifs are shown in Fig. 1b
along with examples of the tutor’s motifs. This bird strongly increased its singing
rate right after tutoring onset (Fig 1d). To compute the similarity of the juveniles’
song motifs to the tutor’s motifs (N=10 motifs each) on each day, we used an
algorithmic procedure to rank all juvenile motifs in terms of spectral and temporal
stereotypy (see Methods). From the tutor we visually selected 10 representative
song motifs. Similarity between a pair of juvenile-tutor motifs was computed using
Sound Analysis Pro (O Tchernichovski et al., 2000). In the four juveniles in which
we were not able to perform a sufficiently reliable syllable clustering, we selected
the 10 best candidate motifs based on visual inspection of spectrograms. For a
given day, song similarity was defined as the average similarity across all 100 motif
pairings. On average, both song similarity and daily singing duration increased
with age, Fig. 2.2b leading to an apparent correlation between the two.
To inspect whether more singing leads to larger increments in similarity, we
analyzed the relationship between the changes in song similarity and the amount
of singing in four ten-day periods, from 50 to 60 dph, 60 to 70 dph, 70 to 80 dph,
and 80 to 90 dph. Combining all these data, we found no significant correlation
between the two variables (Pearson correlation coefficient r = -0.046, p = 0.75, n =
18 birds, 51 data points, figure 2.2c). Because the largest increase in similarity
occurs between the day of tutoring start and 60 dph (roughly corresponding to the
critical learning period), we also closely looked at the changes in similarity and
singing duration and computed the Pearson correlation on a finer temporal
resolution of single days instead of 10-day blocks (figure 2.2d). However, we found
no significant correlation (r = 0.045, p = 0.464, n = 15 birds, 267 data points).
In Figure A.1, we individually plotted each bird’s changes in similarity versus its
singing duration. We found a few birds with significant Pearson correlation (p <
0.05, in figure A.1. (a, b, c, g, j, n)) and further analysis plan is described in the
discussion (2.3).
To assess the variability of daily song similarity values (10 x 10 juvenile-tutor song
paring)), we computed the standard deviationof the 100 similarity values (method
2.1.5, eq. 1) on a day and took the average 𝜎9 across all days from experiment start
until 60 dph (n = 267 days in 14/18 birds, eqs. 2-1 and 2-2; 4/18 birds are not
included due to different tutoring period and duration). The 𝜎9 was 10.08±2.99. In
comparison, during the critical learning period until 60 dph, the average song
change was calculated by using absolute values for all changes in similarity values
across a two-day interval. The overall song change in similarity for the two days
interval was 5.81 and its standard deviation was 5.10. Thus, the standard deviation
in song similarity (n = 267 days, s.t.d = 10.08±2.99) during the critical learning
period was about twice as large as the average absolute song change across two
days for the same period (n = 267 days, 5.81±5.10).
It is known that the amount of tutor song exposure plays a key role in song learning
(O Tchernichovski et al., 1999), therefore we also inspected whether more
exposure to tutor song leads to less increase in song similarity, but found no
significant correlation between total duration of tutor song exposure between days
50 and 60 and similarity increment across that time period (figure 2.2e, Pearson r
= -0.13, p = 0.6, n = 18 birds, 18 points).
2.3. Discussion
In developing zebra finch song, we inspected the relationship between the amount
of singing and the increase in similarity with tutor song. During two weeks, we
provided 90 minutes of daily tutoring and left the juvenile birds to practice singing
on their own. Our relatively simple analysis yields no significant relationship
between song similarity and the latter two factors.
Known factors that have an influence on song similarity increments are the
presence of siblings (Ofer Tchernichovski, Mitra, Lints, & Nottebohm, 2001) and
social feedback provided by adults (Y. Chen, Matheson, & Sakata, 2016). Second,
there are external factors such as temperature, humidity, amount of handling by
experimenters, metabolic activity (Crino, Driscoll, Ton, & Breuner, 2014), and
circadian rhythms (G. Wang, Harpole, Trivedi, & Cassone, 2012).
In conclusion, song improvement is not a simple result of song practice or exposure,
a finding that confirms the recent evidence that practice is not the sole predictor of
success in skillful activities such as sports and music (Macnamara et al., 2016).
While our work suggests researching other factors that may influence song learning,
we believe that our results emphasize the role of the more complex and poorly
understood neural processes underlying motor learning. For example, it has been
shown that variability in motor output is essential for songbirds to adapt the pitch
and duration of their songs in adulthood (Andalman & Fee, 2009; R.H.R.
Hahnloser & Narula, 2017; Kuebrich & Sober, 2014; Tumer & Brainard, 2007;
Warren, Tumer, Charlesworth, & Brainard, 2011) and during development
(Aronov, Andalman, & Fee, 2008; Ölveczky et al., 2005; Tachibana, Takahasi,
Hessler, & Okanoya, 2017). These findings are supported by results in humans
where it was found that task-relevant variability in motor output promotes motor
learning through reinforcement (Wu HG, Miyamoto YR, Gonzales Castro LN,
2014). It is therefore conceivable that a fine-grained analysis of goal-directed
(toward tutor song) versus task-irrelevant (all other) variability in song learning
may provide better predictors of song similarity increments.
Lastly, to get a better sense of the variability in the relationship between song
duration and change in similarity in individual birds, we suggest the following
extension of our experiment and analysis for future work. First, the sample number
of birds should be increased (we suggest n = 20) and the tutoring duration should
be synchronized for all subject juveniles. Then, the group of juveniles should be
separated into good (daily change in similarity is positive) and bad (daily change
in similarity is negative) learners to separately see the change in similarity by song
duration in each group. Second, the high variance of daily song similarity values
(n = 267 daily song similarity values from 15/18 birds) was 10.08±2.99, see result
2.2) should ideally be lower. One idea could be to use only evening songs of
juveniles for song similarity analysis, because it is known that evening songs are
less variable than morning songs (Kojima & Doupe, 2011). In addition, one might
want to try to inspect 3-day block of changes in similarity versus song duration,
because clear changes in similarity can be seen in 10-day blocks in figure 2.2b, and
so clear changes might be visible in 3-day periods as well.
3. Neural dynamics during vocal learning in juvenile 3.1. Introduction
3.1.1. Functional Single- and Two-Photon Imaging in Neuroscience
The nervous system consist of various organizational levels. It is possible to
identify relevant anatomical structures and functional signals on distinct scales
spanning many levels of magnitude, on a temporal scale and on a spatial scale. In
larger vertebrates, for instance, the entire nervous system spans over a range of
meters, or even extends tens of meters, as in the case of whales (Breathnach, 1960),
while the transmembrane ion channels are composed of single molecules within
the range of an Angstrom (10-15 m). The temporal range is impressive as well: an
ion channel goes through conformation changes that induces on and off switching
of ion permeability within fractions of milliseconds (E Neher & Sakmann, 1976),
whereas neural plasticity that guides the growth of an animal happens over years.
Churchland and Seijnowski emphasize the property of nervous systems in ‘The
Computational Brain’ (Churchland & Sejnowski, 1994). Please see the Figure 1.8,
illustrating the spatial aspect of the nervous system.
It is not surprising that just a single method cannot provide insight into structures and phenomena on all temporal and spatial scales relevant for neural systems. As a result, a variety of techniques were developed and used over time to study distinct scales of nervous systems. Each technique covers a specified spatial and temporal range allowing the exploration of only a restricted number of phenomena. This kind of limitation underlies restrictions that are usually impossible to overcome. The circumstance is illustrated in Figure 3.1 from the 1992 publication by Churchland and Sejinowski. Shortly before the publication, Churchland and Sejinowski used the same diagram in Science (Sejnowski, 1988). Even though the diagram seems technically outdated, it shows principal imaging technique development at the time and most of the methods are still relevant nowadays. Denk et al. introduced the two-photon microscopy in 1990 (Denk, Strickler, & Webb, 1990) shortly after Churchland and Sejinowski’s Science publication in 1988. This method had enormous impact on the research of nervous tissue ever since and has been developed for last decades. So far, it could overcome some of the technical deficiencies described in Churchland and Sejinowski’s paper in 1988. We will briefly introduce the development and impact of two-photon microscopy, and its niche occupation in neuroscience field.
Log Time (sec)
Log
Size
(mm
)
Figure 3.1.: The spatial and temporal resolution of a variety of methods applied in neuroscience. Each application used by neuroscientists permits the study of neural phenomena only in a limited spatio-temporal range. Ever since two-photon microscopy (grey-blue area) was introduced by Denk et al. in 1990, it could cover the niche range where the black-and-white diagrams (traditional applications) from 1988 could not incorporate. Figure redesigned from (Sejnowski, 1988), which can be deliberated over reference to the other applications.
3.1.2. Single- and Two-Photon Microscopy
Two-photon microscopy is based on the fact that not only a single photon of proper
energy can excite a fluorophore, but also more than two photons can be absorbed with
proper total energy and thus excite the fluorophore. In 1931, German physicist Maria
Göppert-Mayer first anticipated the option of multi-photon absorption (Mayer, 1931).
She calculated that absorption probability of two-photon relies quadratically on the
photon density, which means it requires very high light intensity. However, it took
several decades until her prediction could be experimentally tested because high
enough photon density production was not possible due to the lack of strong light
sources at her time. Kaiser and Garret provided the first experimental proof for two-
photon excitation (Kaiser & Garrett, 1961).
After excitation with red light, CaF2:EU2+-crystal emitted blue light and attributed to two-photon absorption. Indeed, the result confirmed Göppert-Mayers prediction. Later, the emergence of pulsed femto-second lasers in microscopy applications resulted to a wider use of two-photon excitation. The use of two-photon excitation for a fluorophore in microscopy has several benefits over single-photon excitation: first, two-photon microscopy comes with inherent 3D resolution due to the quadratic reliance on photon density (Denk et al., 1990): Before entering the specimen, the laser lights travel through an objective lens and
is thus focused in a focal point directed at lying within the specimen. Due to its
quadratic reliance on photon density, two-photon excitation in the region of the
focal spot can be restricted to a very small volume. Navigating the focal point
through the specimen then enables targeted collection of information based on
fluorescence light. If easy xy scanning is applied, it is possible to reconstruct 2-
dimensional images. More elaborate excitation targeting method have been
invented so that only images from interesting constructions can be imaged (Goebel,
Esposito, & Formisano, 2006; B. F. . Grewe, Langer, Kasper, Kampa, & Helmchen,
2010; J. Grewe, Wachtler, & Benda, 2011) and thus the temporal resolution can be
improved compared to imaging whole planes or volumes. Second, since the
excitation location is known, there is no need for further sectioning and entire
emitted light can be collected. This offers a significant benefit over confocal
microscopy where pinholes allow sectioning of z-direction but also lead to
undesirable loss of signal. Third, for two-photon excitation, the excitation light
wavelength is typically about twice the one-photon excitation case wavelength.
Lower-energy light induces less photo damage and less dispersion permitting for
longer imaging sessions and, eventually, greater signal gain. And last but not least,
as excitation only occurs around the focal region, photo bleaching can be
dramatically reduced.
Figure 3.2.: Two-photon microscopy utilizes nonlinear fluorophore
excitation. A To reach an excited state, two photons can be almost simultaneously absorbed by a fluorophore. When the excited electron retreat to the ground state, a single photon is emitted. B Linear (in one-photon microscopy) excitation of fluorophores induces ubiquitous excited fluorophores whereas nonlinear fluorophore excitation is able to be spatially hindered to occur in the focal spot only. C Schematic view of a classic two-
a
b
c
photon microscope. To allow two-photon excitation, high enough photon densities are produced by a pulsed laser in the focal spot. The focal spot is scanned through the specimen by means of a xy-scanner. Figure redesigned from (Fritjof Helmchen et al., 2005; Svoboda & Yasuda, 2006).
Since two-photon microscopy is based on fluorophores and since most of
biological specimens are not fluorescent, it is necessary to insert fluorescent probes
into specimens. Fluorescent probes can serve multiple purposes: they can visualize
structure, such as evaluating spine turnover in juvenile zebra finch HVC neurons
using GFP (Roberts, Tschida, Klein, & Mooney, 2010b). Also, they can tag certain
structures: e.g. by injecting tracers to explore neural projections or by immune-
labeling of object molecules (Oberti, Kirschmann, & Hahnloser, 2011). But
fluorescent probes are also able to serve as cellular activity and cellular states
detectors. The fluorophores in these applications are mostly coupled to
macromolecules that goes through conformational modifications in response to pH,
altered ion concentrations, or voltage. The conformational modification of the
sensing macromolecule influences the fluorescence of the fluorophore afterwards.
Two different approaches with regard to neural activity have been undertaken thus
far: sensing changes in the concentration of intracellular ions and direct sensing of
membrane potential. Sensing changes of the concentration of intracellular ions is
challenging for two reasons: first, even though the electric field required to cause
a conformational change for sensing macromolecule is greater in a single in many
orders of magnitude, it is very close to ion as compared to the weaker electric field
crossing over the cell membrane because of Coulombs law. Second, to detect
voltage alteration across the cell membrane, probe molecules have to be located in
cell membrane but not in membranes of cell nucleus, endoplasmic reticulum or
mitochondria, as background fluorescence should be avoided as far as possible.
Introducing molecules into the cytosol of cells is less complicated than guiding
molecules into the right membrane point. Therefore, direct introduction of
fluorescent calcium indicators into cells developed as the most useful reporters of
neural activity.
3.1.3. Fundamentals of Calcium Imaging Calcium ions cover a broad variety of essential functions in living organisms and especially in neural structures2: calcium ion induces the release of neurotransmitter vesicles in presynaptic boutons (Erwin Neher & Sakaba, 2008). On the other end of the synaptic cleft, short-term rises in calcium concentration in post-synaptic dendritic spines result in synaptic plasticity (Zucker, 1999). Calcium nucleus was shown in the cell to control gene transcription (Lyons & West, 2011). Finally, neural activity associated with modifications in the concentration of intracellular ions also involves calcium ion concentrations (Schiller, Basch, & Blanc, 1995). Changes in the concentration of Cytosolic calcium are facilitated by various
mechanisms. Action potential associated with calcium increases was expressed to
include voltage-gated calcium channels (Schiller et al., 1995; Yuste & Denk,
1995). Also, ionotropic receptors such as N-Methyl-D-Aspartate (NMDA) or α-
Amino-3-Hydroxy-5-Methyl-4-Isoxazolepropionic Acid (AMPA) mediate the
flow of calcium transmembrane and metabotropic glutamate receptors. There is
also significant intracellular storage of calcium that released from both
endoplasmic reticulum and the mitochondria. Finally, excess calcium from the
cytosol is removed by the plasma membrane ATPase and the sodium-calcium
exchanger.
First optical calcium measurements were accomplished with indices, which, from
today's aspect, work in unexpected ways:
For instance, Aequorin (Shimomura, Johnson, & Saiga, 1962) is a bioluminescent
indicator which emits photon on calcium binding and does not require external
illumination; however, photon emission can occur only once per molecule. Another
indicator is Arzenazo III: it shifts its absorption spectrum by binding calcium ions.
Introduction of fluorescent calcium indicators rendered a quantum leap. Roger
Tsien and his lab were key contributors to calcium sensing. They developed
synthetic, calcium-sensitive Chelator based fluorescent dyes, the calcium green and
the fluo series including the fura (Tsien & Pozzan, 1989). Numerous synthetic
2 A detailed review of calcium imaging in neurons can be found in (Grienberger & Konnerth, 2012) and also described in introductory section of this chapter.
calcium indicators have been developed with various absorption and emission
spectra along with varying kinetics.
In addition to the bulk loading with the process of cell populations, extra delivery methods have been created for synthetic dyes. Image activity from recognized projection neurons was enabled by Tract loading with dextran-conjugated calcium indicators tracked by retrograde neurons (O’Donovan, Ho, Sholomenko, & Yee, 1993). Two types of dye electroporation were introduced: 1) local electroporation that scarcely labels individual neurons near the injection pipette (Nagayama et al., 2007), and 2) single-cell electroporation; a method that allows one cell at a time to be targeted (Nevian & Helmchen, 2007).
The use of Genetically Expressed Calcium Indicators (GECIs) introduces another method of indicator delivery. GECIs are integrated by the cells themselves utilizing guidance given in the form of DNA. Using custom-designed viral vectors that are shot into a region of interest where cells are infected can introduce DNA into cells. The infected cells integrate the DNA genes in the vector and show the indicator3. GECIs of various types have been produced. Improving their practicality in
neuroscience continues to be a subject of high interest in current research. As
shown in Figure 3.3, two GECI kinds of different working principles are described.
The first type is the single-molecule-based of GECIs that is composed of one
fluorophore paired with a calcium sensing molecule. At the sensing molecule,
calcium-binding modulates the fluorescent properties of the fluorophore. Such
indicators as GCaMP family, for example, consists of a GFP derivative,
Calmodulin, and an M13 peptide. Calmodulin and M13 peptide for a complex after
calcium-binding and thus boost GFP fluorescence. The second type of GECIs
includes two fluorophores joined as a donor-acceptor pair of Fluorescence
Resonance Energy Transfer (FRET) and a calcium sensing molecule. Calcium-
binding causes changes in the effectiveness of transfer between the two
fluorophores and alters the fluorescence of both. One instance is the calcium
3 DNA can also be transported to an organism in-ovo (Nakamura & Funahashi, 2001) or in-utero (Saito & Nakatsuji, 2001). By now, transgenic lines of target animals are obtainable through expressing different indicators or dyes in restricted cell types (Hasan et al., 2004).
indicators of the Yellow Cameleon (YC) family4.
Figure 3.3.: GECIs are built on two primary principles. a A single fluorophore-based GECI (GCaMP in the figure) consists of a fluorophore (GFP) in mixture with calcium sensing Calmodulin which constructs a complex on calcium binding with the M13 peptide and thus increasing the GFP fluorescence emission. b Two fluorophores create a FRET pair putting Calmodulin and an M13 peptide together. Calcium binding results in modulation of the FRET efficiency, which changes the fluorescence qualities of both fluorophores. The illustration indicates the Yellow Came-leon (YC) indicator. The figure above is taken from (Hires et al., 2008).
Calcium Transients Serve as a Proxy for Neural Activity
Sharp rises in levels of intracellular calcium are led by action potentials (F
Helmchen, Imoto, & Sakmann, 1996; Svoboda, Denk, Kleinfeld, & Tank, 1997).
These calcium transients' declining constants are significantly larger than those
of APs. However, calcium signals have been shown to add up until saturation is
4 Please find the genetically encoded indicator reviews of neural activity in (Hires, Zhu, & Tsien, 2008) and (Looger & Griesbeck, 2012).
reached when trains of APs occur. AP train frequency is the determinant of
saturation levels (F Helmchen et al., 1996). Therefore, the connection between
AP and calcium transient from calcium recordings allows readout of neural
activity. Figure 3.4 shows evidence for this connection.
Figure 3.4.: Calcium signals enable AP to be readout. A Calcium transient
with a sharp increase and a slow exponential decline accompany APs. V pyramidal neuron trains of APs guide to the calcium signal summation in this rat layer. The amount of saturation connected with APs high-frequency trains relies on the train’s frequency. Activity is feasible under these properties’ conclusions of calcium neural activity. Figure A is taken from (Helmchen et al., 1996). B The close link between the spike train and calcium trace allows the reconstruction of spike trains. The measurements are shown in black high-speed calcium. The overlaid blue curve represents a calcium trace estimation based on a model with the respective spike train below in blue. The estimation of spike times is very accurate as shown by the comparison with the contrast with the neuron stimulation times in red. Figure B is adapted from (B. F. Grewe et al., 2010).
At the same time, Grewe et al. measure calcium signals at high velocity and
juxtacellular electrical activity (B. F. Grewe et al., 2010). These high-resolution
data enabled precise description of the calcium transient associated with a unitary
AP. Their calcium trace model at time t0 with a single AP consists of a sharp
exponential upturn and a double exponential decline:
f1AP (t) = (1−e−(t−t0)/τon))(A1e−(t−t0))/τ1 + A2e−(t−t2)/τ2) (1.1)
Using this model in conjunction with a Schmitt-trigger based ‘peeling’
algorithm, they attain nearly millisecond of accuracy in spike detection, see
Figure 3.4b.
Yaksi and Friedrich suggested using this model in two ways, to convert a spike
train with a single AP model to acquire the calcium trace, and also to deconvolve
the calcium trace using a single AP to rebuild the firing rate (Yaksi & Friedrich,
2006). Other innovations in the field of spike train reconstruction from calcium
imaging data are the inference of calcium imaging data connectivity (Mishchenko,
Videen, Rosenbush, & Yatskiv, 2011), use of Monte Carlo methods and the
limitation to non-negative firing rates (Vogelstein et al., 2010, 2009).
However, the fact that acquired calcium traces oftentimes experience a low signal-
to-noise ratio cannot be covered by all of the above-described algorithms. Both
method inherent noise and instrumentation noise add jitter to the smooth calcium
traces so that the experimenter is constantly pressured to enhance the number of
cells imaged, signal quality and the data acquisition frequency (Lütcke &
Helmchen, 2011). Therefore, it is of great value that the imaging system has been
established that enables non-classical access to area of interest, such as using
acousto-optical deflectors to scan random access pattern (B. F. Grewe et al., 2010)
or z-direction scanning, using a piezoelectric stage for the objective lens (Goebel
et al., 2006).
But even using conventional image scanning, single AP readout of two-photon
microscopy is feasible under maximum circumstances using synthetic dyes in some
systems and it has revealed by now that GECIs provide an adequate signal for the
reliable detection of single AP (Tian et al., 2009).
To summarize, two-photon calcium imaging is a highly useful, ever-developing
experimental method that allows concurrent monitoring of neural activity from
countless cells in spatial vicinity.
It is not surprising that over the last two decades, the combination of calcium
sensitive fluorescent dyes and two-photon microscopy has been used intentionally:
Yuste and Denk’s study is one of the first research using two-photon microscopy
and calcium sensitive dyes. In rat brain slices, they recorded dendritic spines of
hippocampal CA1 pyramidal neurons (Yuste & Denk, 1995). They detected several
spontaneous post-synaptic events limited to single spine heads: backpropagating
somatic action potentials induces large volume of calcium increases in dendritic
spines, somatic action potentials, and coincidence synaptic event detections inside
of single spines. Dendritic spines were thus recognized as fundamental parts of
neural processing.
The beneficial characteristics of two-photon microscopy were transmitted in intact
structures to further research of neural activity in vivo (Svoboda et al., 1997). A
further significant move was the development of guidelines for the bulk loading of
synthetic calcium indicator cells (Stosiek, Garaschuk, Holthoff, & Konnerth,
2003a). This implied that the functional imaging of in vivo neural populations with
single cell resolution was ready. As explained by Churchland and Sejnowski at the
end of the 1980s, the lack of comprehensive data on the neural network process within
cortical layers and columns could now be resolved by using this novel method:
Ohki et al. performed functional calcium imaging in the anesthetized cats and rats’
visual cortex (Ohki, Chung, Ch’ng, Kara, & Reid, 2005). They investigated the
columnar organization of responses with single-cell resolution to the stimulation
regard to the moving gratings. Within a cat’s cortical column, neurons had already
been known to respond selectively to gratings moving in the same direction, i.e.
they have the same direction preference. Ohki et al. observed three distinct
columnar micro-architecture regimes: First, in cat area 18, a generally smooth
transition of directional preference throughout the cortex was observed. The
distinction of orientational preference in neuron pairs rises with the gap between
the neurons. Second, extraordinarily sharp borders, only one to two cells wide,
were observed at points of direction discontinuities. Third, the distance between
neuron pairs in rat visual cortex did not correspond with the opposition in their
selectiveness of direction. Rat visual cortex is therefore sometimes referred to as a
‘salt-and-pepper’ organization. The discovery in visual cortices of finely tuned
response maps now opens new questions about the processes leading to the
spatially accurate structure of response properties. Furthermore, it highlights the
significance of studying structural and functional nervous tissue organization with
high temporal and spatial resolution.
The olfactory system of the zebra fish was imaged by Niessing and Friedrich
(Niessing & Friedrich, 2010). In response to the presentation of odors at various
levels, they evaluated activity patterns of mitral cells. Patterns of mitral cell activity
were reorganized soon after stimulus presentation. At distinct but similar levels,
activity patterns for the same odor were correlated while activity patterns for
distinct odors were decorrelated. Morphing one odor into another led in sudden
shifts in activity patterns from one intermediate concentration to the next. This
network conduct promotes an odor classification attractor model.
Furthermore, they observed that only a small proportion of mitral cells facilitate
sharp transitions from one network state to another. They could only make such a
finding since they were able to simultaneously monitor from many cells.
Ultimately, studying neural activity in awake behaving animals is of great interest
to neuroscientists. In awake animals, two pathways were taken to allow two-photon
calcium imaging. First, two-photon miniature head-mountable microscopes have
been designed (Sawinski et al., 2009). Compared to conventional two-photon
microscopes, they were only able to image from a very restricted field of view and
thus partially losing the benefit of monitoring many cells. Second, studies have
been carried out with awake head-fixed animals (Dombeck, Khabbaz, Collman,
Adelman, & Tank, 2007; Georg B. Keller, Bonhoeffer, & Hübener, 2012;
Komiyama et al., 2010) and even recently with awake head-fixed zebra finch
(Picardo et al., 2016b) after motivated singer selection. While animals stayed head-
fixed, they could participate in various activities, such as running on an air-
supported Styrofoam ball (also in conjunction with controlled visual feedback) or
learning a task of choice (Dombeck et al., 2007; Georg B. Keller et al., 2012;
Komiyama et al., 2010). Pircardo et al. had to undergo several months long training
and selection procedure to collect singing awake head-fixed zebra finches under
two-photon microscope. Even though they could use only 6 singing birds out 109
trained birds for two-photon imaging, it is still meaningful first step that zebra
finches can be a good target for two-photon calcium imaging experiments.
3.1.4. Calcium Imaging in the Zebra Finch
Since Nakai introduced GCaMP, one type of Genetically Modified Calcium
Indicator (GECI) in 2001 (Nakai, Ohkura, & Imoto, 2001), it has been extensively
used in calcium imaging studies. In recent songbird research, calcium imaging in
awake zebra finch delivered valuable information in HVC during song learning
and production. Last several years, intriguing reports using single-photon light
weight microscope (Liberti et al., 2016; Markowitz et al., 2015) and two-photon
microscope (Picardo et al., 2016b; Vallentin et al., 2015) - revealed principal neural
mechanisms in song production and learning. However, due to technical issues in
both microscopes - single-photon microscope requires head-carrying and tethering
and two-photon microscope requires head-fixation of the subject and, imaging in
freely behaving zebra finch, especially in juvenile zebra finch (since their skull is
very fragile to be fixed nor head-implanted with microscope), has been a
challenging project. Moreover, virus injection of AAV-type is required about 20
days prior to the beginning of imaging to induce enough expression to be seen
under microscopes.
Indeed, the first goal of the second part of this PhD thesis was therefore to establish
the applicability of in vivo single- and two-photon calcium imaging in juvenile
zebra finch brain during vocal learning period - about 45-60 dph. We restricted our
target region to the premotor area HVC, which is located superficially enough near
the posterior pallium to grant direct optical pass (Roberts et al., 2010b). We
approached the longitudinal imaging in HVC with two different prerequisites,
always with the ultimate goal in mind to record calcium signals from populations
of neural activities simultaneously:
First, we explored available GECIs which show good expression in the zebra finch.
We tested several AAV-related virus constructs and various indicator proteins
expressed under several different types of promoters. We found appropriate
combinations which brilliantly expressed for more than a month in HVC of juvenile
zebra finches.
Second, we examined whether the juvenile birds could learn a song by tutoring
undergone with imaging sessions. For single-photon calcium imaging, juvenile had
to carry the implanted device (~1.7 g). On the other hand, for two-photon calcium
imaging, juveniles were implanted with very light weight head-fixation material
(~0.5 g) but had to be head-fixed during imaging sessions (1.5 hrs per day for max.
two weeks). We found out some of the juveniles could learn from a tutor song
under stressful imaging conditions with tutoring.
3.2. Methods
3.2.1. Virus injection To induce target GECI in the target brain area, virus carrying target GECI is
injected in the brain. Virus injection for juvenile was performed 20 days prior to
the first imaging - aiming robust expression of target protein (GCaMP6, GFP, etc)
from 40~45 dph. All following surgical procedures were performed under 1.5-2.5%
isoflurane anesthesia in oxygen. At an angle of 45 degrees of the head-fixing ear
bar to beak, birds were anesthetized and retained in a stereotaxic device (Narishige
Group). After removing the head feathers, 5% lidocaine cream was applied on the
bare skin to reduce a pain. To precisely navigate the target area of injection, head
skin was opened by surgical scissors. At 20-35 dph, skull of juvenile zebra finch is
monolayer and thus extremely soft and main blood vessels are visible right under
the skull. Therefore, usage of surgical blade is forbidden for incision. To induce
least damage while opening the head skin near the target area, mainly HVC, skin
near a beak was carefully lifted and open by a surgical scissor along the medial line
(see figure 3.5).
Figure 3.5. Incision for targeting virus injection in HVC. 𝜆 point (corres-
ponding to bregma of rodents) as the stereotactical center, target injection point
(HVC in this figure) has been navigated after incision. Skull of juvenile bird is
extremely fragile thus a surgical scissor has been used to open the head skin. Using
sharp forceps, a small opening (~ 10um) on the skull was made. A glass pipette
filled with a small volume of virus (~ 2 ul) is used to penetrate the dura and reach
the target injection point.
Based on the HVC coordinate (Medial-Lateral: 2.0 mm (for both left and right
hemispheres), Anterior-Posterior: 0.5-1.0mm) as reported in a previous study
(Karten et al., 2013), a glass pipette filled with 1-2ul of viral solution is located on
top of HVC by stereotaxic. Before injection, 200 – 300um size craniotomy is made
by using a sharp tip of surgical mess. Dura is generally penetrated without breaking
the glass pipette tip with perpendicular insertion. After penetrating dura, the glass
pipette is located 1.0 mm deep from the surface and pulled back for 300um to make
a tiny cylindrical trunk to fill up a viral solution in the deeper region. For the
injection, nanojector or picospritzer is used to make a programmed injection
protocol: every 1-3 second, 1-2nl viral solution is slowly injected until injected
volume reaches to the target volume (200-300nl). The glass pipette is retreated for
two more times for 300um each (so 700, 400, 100um deep from the surface) with
volume of 200-300nl in each spot and time interval of 10 minutes per spot. When
injection is finished, all skull openings are closed by their own bone pieces. The
head skin is closed back to cover the area and resealed with tissue glue (Adapted
from RHahnloser SOP: 4 Brain Surgery).
𝜆𝑝𝑜𝑖𝑛𝑡
Targeting
HVC
3.2.2. Tracer injection To test specific labeling of projection neurons in HVC, tracers such as Dextran
Texas-red or Dextran Cascade-blue are injected in RA and/or Area X for 2 birds.
We inject the tracers ~5 days before imaging for waiting enough retrograde
transportation time through axons.
Figure 3.6. Diagram for virus and tracer injection scheme to distinguish
Projection Neurons (PNs) from interneurons in HVC. GCaMP6 is
expressed in some HVC neurons and red dextran tracers are retrogradely
transported through axons of the PNs. Yellow neurons are assumed as PNs
that GCaMP6 (green) expressed as well as Dextran Texas-red (red) stained
neurons.
3.2.3. Head plate and cranial window implantation
To head-fix the bird during two photon imaging, a light metal head-holder (< 0.5
g) is implanted into the skull. For live and longitudinal recording of the neuronal
activity, up to two cranial windows (L/R hemispheres) are implanted before
imaging (Adapted from R.Hahnloser SOP: 7.1 Head plate implantation).
HVCRA
HVCX
Interneuron
Tracer Inj.: 40 dph
AAV- GCaMP6 inj.: 25 dph
100um
3.2.4. Miniscope implantation in juvenile zebra finch
Miniaturized fluorescent microscope (miniscope) is used to image neuronal
activity with single cell-resolution in freely moving birds (Ghosh et al., 2011;
Markowitz et al., 2015). The maximum weight of the miniaturized microscope is
2.0 g (Liberti et al., 2016). Particularly for 40-50 dph juvenile zebra finch,
customized 1.7g miniscope is implanted on the skull after anchoring protocol (See
RHahnloser SOP: 7.5 Miniaturized fluorescent microscopes in freely moving
animals): Drill in the skull with two small holes (0.5 mm) separated by a distance
of 2.0-3.0 mm (hole pair). Previous step is repeated for 2-4 times. 100 um thick
insect pin is inserted into one hole of a hole pair at an almost horizontal angle. The
pin is advanced so that one end sticks out through the other hole. By using two
forceps, each end side of pin is pulled out and bent for 90 degrees. Small amount
of dental cement is applied on each pin. For robust fixation of miniscope, optionally,
200-300um thick 3D printed stage could be quickly placed on the dental cement
before solidification. Otherwise, more dental cement can be applied around the
cranial window to make a support structure for miniscope implant.
Since HVC is deeper than 200um from the brain surface, neural tissue above the
target area is removed by forceps and a scalpel. After slight slicing (<200um) on
the central target area from anterior to posterior for 1mm, top layer of the target
areas is pushed to left and right by using wet cotton. If blood comes out from the
opened surface, let the wet cotton sucks up the blood for at least 1 minutes until it
stops. Then the pulled top layers can be removed by forceps.
Fully assembled miniscope is located onto the miniscope stage while the weight-
reliever (in figure 3.7b, kwik-sil attached tooth-pick on the left top held by metal
holder) mechanism is active (See RHahnloser SOP: 7.5 and Figure 3.7)
a b
Figure 3.7: Mounting of the miniscope onto the miniscope stage (labelled ‘stage’,
left) or on top of dental cement stage (right).
3.2.5. Calcium imaging
We perform longitudinal imaging of neuronal ensembles with our custom built
two-photon laser scanning microscope powered by Ti:sapphire laser (Spectra
Physics) in a head-fixed preparation.
We mainly target the central parts of HVC, roughly 2.4 mm lateral of the sagittal
sinus and 0.7 mm anterior of the coronal sinus. As soon as we see fluorescent
neurons with right focal plane, we start scanning all regions where we can find
maximal number of neurons and mark their coordinate displayed in the motor arm
manipulator (Sutter instruments). After selecting the specific region of interest
(ROI), we perform longitudinal imaging on the same region for up to 40 days on a
75 days period. We acquire frame scans of 512x512, 256x256, 128x128 pixels in
380 x 380 µm2 size of ROI with >9 Hz scanning rate. To discriminate neural
activity corresponding to short-time manner of syllables (50~200ms), we mainly
use 9.32Hz for temporal resolution which provides 128x128 pixels for spatial
resolution automatically. Acquired images are stored to disk by Scanimage 3.8
software (Janelia farm) written in Matlab.
3.2.6. Sound recording and tutor song presentation
Sound recording
Birds are raised with a mother until 35 dph and later transferred into a smaller cage
in a sound proof chamber to obtain high quality song recording. They are
accompanied with a female until the end of the experiment. Their songs are
recorded by a wall microphone (Pro 42), digitized at 32 kHz sample rate, band-
pass filtered in the range 0.5~8 kHz, and saved to disk using our customized
recording software written in LabView (National Instruments).
For two-photon imaging, we bring the juvenile with a tutor to the two-photon
chamber in a laser room. The onset of the imaging is triggered by the tutor’s singing.
Each imaging is performed for 10~15 seconds to include both singing and silence
after onset. To match up the onset and offset of the singing with the saved imaging
file, any sound during imaging is also recorded by the same microphone setup from
the sound proof chamber.
Tutor song presentation for head-fixed juvenile
Tutoring during two-photon imaging: During vocal learning in juvenile, we
schedule two-photon imaging on juvenile to observe the short impact of the tutor’s
song. Prior to the first imaging, we train the juvenile bird with head-fixation for
less than 2 hours per a day, for up to 5 days. During the imaging session in each
day, the awake bird is head-fixed to minimize head movement but in an upright
position on a perch with a freely moving condition. During the entire imaging
sessions, we provide a live tutor next to the head-fixed juvenile. In our setup in the
2PFM chamber, a live tutor’s singing triggers the laser scanning of the microscope
so that the neuronal activity while the singing duration of the tutor is saved at all
times. During < 2hrs. of the imaging session, all sound from the 2PFM chamber
will be recorded for analysis with the neuronal recording.
3.3. Data analysis 3.3.1. Song similarity We use Sound Analysis Pro (SAP) to caclucate similarity of two different songs
such as juvenile songs and tutor songs (see Method part 2.1.5).
3.3.2. Single and two photon imaging
Two different types of imaging, single- and two-photon imaging, were performed.
For single-photon imaging, Using customized software written by Processing
(Written by R. Tachibana) and Python, whenever proximally located tutor sings to
the miniscope implanted juvenile, neural activity in HVC is recorded by 29.97 fps
to computer disk. For two photon imaging, using Scanimage 3.8 (Janelia farm)
written by Matlab (Mathworks), neural activity in HVC is saved to disk by up to
19.6 Hz during tutoring or loudspeaker playback. For both types of imaging, at the
same time of the image saving, sound recording file is saved to another disk
according to the onset and offset timing based on the laser scanning triggering. To
study population level of neuronal activity in HVC, we plot ΔF/F traces in broad
populations of our target areas. We detect fluctuation peaks of the ΔF/F traces
based on a certain threshold level and define the firing timing. In the end, we plot
the detected firing event of the neurons in raster plot together with the singing
duration of the tutor.
3.3.3. Statistical analysis for identified neurons.
To identify and analyze all significantly responsive neurons to the tutor song in the
ROI, we perform a three-stage analysis as in (Glickfeld, Andermann, Bonin, &
Reid, 2013; Picardo et al., 2016c):
First, to identify all active neurons, we label somatic shaped areas with multiple
fluctuation peaks of the ΔF/F0 (defined as ΔF = (F – F0), and F0= 2-3 s silence
period after minimum 5 s from offset of tutor singing) traces within the 10-20 s
recording trial. Peaks of the ΔF/F0 traces are detected when it has clear action
potential shape and determined for firing timing. In the end, we plot the detected
firing event of the neurons in raster plot together with each motif of the singing
duration of the tutor.
Second, we collect all tutor song related imaging frames of the labeled neuron and
calculate mean (SM) and error bar (standard deviation (SSTD)/√𝑛 (n = number of
used imaging frames) of ΔF/F0 across frame (or time). The non-tutor song (silence)
related imaging frames are collected, and their mean (NM) and error bar (standard
deviation (NSTD)/√𝑛) of ΔF/F0 is calculated as well. The responsiveness to the tutor
song of the identified neurons are defined by statistical comparison test (t-test) for
the acquired means and standard deviations.
Finally, only tutor song responsive neurons are considered for further analysis in
population level. The tutor song is segmented and labeled into syllable level by
using MATLAB software. All responsive neurons during each syllable time
window are assigned to the labeled syllable and grouped together. Each identified
neuron is labelled on the first day of imaging and can be revisited by next day by
locating the manipulator to the saved coordinate from the previous day. We next
cross correlate all the grouped neurons’ responsiveness to the tutor song by daily
basis.
This three-stage analysis is performed during critical learning period on the same
ROI. We expect that identified neurons in HVC show higher neuronal activity and
more time-locked activity in good learners (producing >70% similarity score with
tutor song) compared to bad learners (producing <70% similarity score with tutor
song).
3.4. Results 3.4.1. Longitudinal quality control of cranial window For longitudinal imaging more than 60 days, performing high quality cranial
window implantation is the key technique. After dura removal on the cranial site,
we gently push a thin cover glass (100μm thickness, Fine Science Tools) with a
small toothpick on top of HVC. Next, we seal the surroundings with cyanoacrylate
glue to prevent dura regrowth. From this technique, we could achieve stable
longitudinal imaging on our ROI for up to 70 days covering the whole vocal
learning period.
70 days after implant 5 days after implant
Figure 3.8. Static quality of cranial window for more than 2 months. Red
arrows show the split point of the major blood vessels, which could be
indicators to compare two different days with similar landmark.
3.4.2. Longitudinal neural imaging In order to record longitudinal neural activity, daily neural data acquisition from
the same identified neuron is required for multiple days. For the first trial session
of longitudinal imaging, 45 dph juveniles (n=3 birds; b3p1, b3o14, b3o15) were
imaged during a live tutor (n=1 bird; r12s15) was singing by the head-fixed
juvenile. On the first day of two-photon calcium imaging for the subject, coordinate
of the identified neurons (n=9 for b3p1, n=3 for b3o14, n=1 for b3o15) are labeled
in the first image stacking. On the next day or several days of each imaging, we
locate two-photon manipulator on the same coordinate from the previous imaging
location for the target neurons to find out the same identified neurons. However, it
is possible that neurogenesis could be actively occurring in the juvenile (48~90 dph)
brain so that the location of the identified neurons can be slightly changed day by
day. Therefore, we manually scan the coordinal location of the previously recorded
neurons by moving around the imaging site carefully. If we find the same region
of interest, we start new session of imaging. So far, total of 14 days of daily 2 hours
neural imaging session with a live tutor singing has been performed on the same
identified neuron. In figure 3.9, neural responses during tutor singing and non-
singing (silence) have been recorded for 14 days period. The comparison of neural
activity during singing and non-singing are shown for 4 discrete days to present
changes. In each 2 hours session per day, the tutor sang around 30~50 renditions.
Each singing triggered 15 seconds scanning of imaging with 2 Hz, which results
30 frames image stack with beginning of the tutor song on the early frames. The
daily neural imaging data is analyzed together with the song learning performance
shown in figure 3.9. We are on the early stage of data analysis of neural and song
data acquired from three different head-fixed tutored juveniles.
a
b
Figure 3.9. Longitudinal neural activity recorded more than 60
days from the first imaging at 48 dph (D-1). a. Recorded premotor
neuron activities in HVC at D-1, 12, 37, and 67. Based on the saved
coordinate by the manipulator arm of microscope, target ROI can be
revisited longitudinally. The target ROI is set for the proximal site to
neurons numbered as 1 and 2 (red marked). Neuron no.1 was traced
0
50
100
150
200
250
1 5 9 13
Mea
n ∆f
/f
Days since first tutoring
Mean ∆f/f of juvenile duringTutor Singing vs. Silence
Singing
Silence
D-1 D-12
D-37 D-67
100um 100um
100um 100um
1
2
1
2
1
1
for > 2 months. Activity of neuron no. 2 was disappeared due to
apoptosis from D-16 and new neurons (yellow marked) were appeared
by active neurogenesis in young age (45-90 dph). b. The exemplary
activity of neuron 1 for extensive recording duration for 13 days. Each
recording trial is triggered by tutor's singing and recorded for 15s.
Recording when tutor was singing is defined as ‘Tutor Singing’ and
rest of the 15s trial is defined as ‘Silence’ (Non-tutor singing). To
show change of neuronal response during vocal learning, mean ∆f/f
of tutor singing and silence duration are compared to each other for 1,
5, 9 and 13 days since first imaging day (48 dph).
3.4.3. Tutoring during head-fixation We examined juveniles’ song imitation performance depending on different head-
fixed tutoring status. We divided the groups based on the 4 different experimental
setups: Tutoring with 1) >100 motifs playback for 2hrs. per day for 5 days (4 birds
used), 2) a female presentation and ~20 motifs playback for 2hrs. per day for 15
days (2 birds used), 3) ~20 motifs playback for 2hrs. per day for 15 days in a dark
two-photon chamber (4 birds), and 4) ~20 motifs playback for 2 hrs. per day for 15
days in a dim lighted two-photon chamber (2 birds). Group 1) and 3) (total of 8
birds) show low song similarity to the tutor song. From the group 2), one bird shows
rather high song similarity to the tutor song compare to the other group of birds.
In figure 3.10, it is shown that song learning trajectory can reach ~70% during
head-fixed imaging by tutoring with a live tutor. This implies that the juvenile has
a possibility that he can learn a tutor song through tutor song playback from a
loudspeaker with a female presentation. However, we want to have high song
duplication rate (>80%) because we want to record daily (or occasional) change in
neural activity when the bird’s own song is getting closer to the tutor song during
vocal development. Therefore, we will not proceed head-fixed tutoring during
imaging anymore. Instead, we will tutor the juvenile with a live tutor for around
1.5 hr. before imaging, then perform two-photon imaging.
Figure 3.10. song learning performance during vocal learning
period. Juveniles were tutored with a live tutor during head-fixed
imaging session for 2 hours/day. at day 0, juveniles were 48 dph and
tutored while head-fixed for imaging for 11~15 days depending on
experiment criteria. in this figure, b3o15 has been head-fixed tutored
and imaged for 11 days and b3p1 for 14 days.
3.5. Discussion To fully understand the whole neural network changes in juvenile induced by
tutoring, it is essential to record neural activities of all premotor neurons in HVC
for the entire vocal learning period. However, even with state-of-the-art neural
imaging techniques, only several percentages of neurons can be recorded for a
limited duration. In this PhD thesis, we focused on three important prerequisite
factors to improve the understanding of the dynamics of premotor neural activity
in juvenile birds during vocal learning: 1) labeling of premotor neurons, 2)
longitudinal neural activity recordings covering the critical learning period, and
3) imaging starting from a young age.
Labeling of premotor neurons
CaMKIIa is expressed specifically in premotor neurons but not in interneurons
(Dittgen et al., 2004; Mcmaster, Kristinsson, Turesson, Bjorkholm, & Landgren,
20
30
40
50
60
70
80
90
-1 1 3 5 7 9 11 13 16 19
song
sim
ilarit
y (%
)
days since first head-fixed imaging and tutoring
b3o15
b3p1
2010). To induce the target protein (e.g. GcaMP) in premotor neurons, we
injected AAV-GcaMP combination with CaMKIIa promotor in HVC. By doing
so, only premotor neural activity is observed during imaging.
For more advanced studies such as imaging of interneuron activity together with
premotor neurons, we plan to test different colored fluorophore induction e.g.
AAV-RcaMP with ‘mDlx’ promotor, which is expressed only in interneurons
(Dimidschstein et al., 2016; Mcmaster et al., 2010).
Neural activity recording covering the critical learning period
To observe fluorescence change of neurons, photon incidence is required on the
fluorophores induced in neurons. Since continuous photon incidence generates
heat and bleaching, imaging duration must be controlled to avoid the damage on
the neuron. For both single- and two- photon calcium imaging sessions, we set
up a condition that each imaging session is triggered immediately by a tutor song
or by a song of a juvenile. In general, a tutor sang 50-100 song bouts in both
single- and two- photon imaging. Juvenile sang less than 50 song bouts in single
photon imaging and didn’t sing much in two photon imaging due to head-fixation.
In this condition, imaging quality was well maintained for weeks covering the
critical learning period.
Imaging starting from a young age
It is assumed that the sensory period of zebra finch is closed around 60 dph. Even
though it is a critical period to investigate, there has been no report of neural
imaging before 60 dph in songbirds due to the technical reasons. To observe
neural activity changes by from a week or two weeks of tutoring, we had to
design 46-53 dph as a starting point for imaging. As GECI requires about 2-3
weeks to be visibly expressed in neurons, we injected viral vectors with GECI at
20-25 dph at the earliest. We established new techniques for virus injection in
juvenile songbirds and was able to detect neural activities in both single- and
two- photon imaging from 45 dph. Moreover, we could make a longitudinal
neural imaging of premotor neurons covering the critical period of juvenile in
the end.
To understand the correlation between neuronal activity change and song change
during song development, it is essential to establish longitudinal imaging
technique covering the critical vocal learning period. We have established five
crucial pre-requisites to successfully perform longitudinal imaging: 1) virus
injection at age of 20-25 dph with successful GCaMP6 expression, 2) clear
removal of dura covering HVC, then implanting unclouded cranial window, 3)
identifying and labeling as many neurons as possible on the first day of imaging,
4) daily ROI marking by saving the coordinate displayed in the manipulator, and
5) certain duration (10-15 s) of imaging triggered by a live tutor song or a subject
juvenile. By conducting above pre-requisites, the user of this technique will be
able to record projection neurons’ activity in HVC during song development.
Finally, we expect that our longitudinal imaging in HVC during song
development would answer three critical questions in songbird research. The
correlation between the neuronal activity change and vocal learning performance,
neural activity difference between good and bad vocal learners, and lastly, the
firing sequence development during vocal learning period.
4. Genetically encoded calcium indicators
evaluated in the zebra finch 4.1. Introduction
Compare to mice, eligible virus pool for zebra finch is not well established. To find
out the best candidates for our experiments, we test various kinds of viruses in
zebra finches. We used eGFP, and GECI (Genetically encoded calcium indicator,
see 3.1.3) tagged by various promoters targeting specific proteins expressed in
neurons.
4.2. Method A modified version of general virus injection protocol has been used for target virus
injection in juvenile bird due to a fragile skull at age of 20-35 dph (See RHahnloser
SOP 4.4).
4.3. Results After intensive searching experiments, we found out a list for available viruses and
their efficiency (See Table 1):
Promoter Serotype eGFP GCaMP6s GCaMP6f
CAG
AAV1 NT + +
AAV5 NT +++ NT
AAV9 NT ++ +++
CamKIIa AAV9 NT +++ NT
Syn AAV5 NT NT +
DJ
V24 - NT
NT V64 - NT
V65 +++(SC) NT
V56 +(SC) NT Table 1. Expression result from various combinations of serotype, promotor, and
fluorescent indicators. Red filled block are the best candidates for target expression. NT:
Not tested (doesn’t exist in the lab or too old virus). SC: Self Complementary.
AAV-DJ (cocktail of several (1,2,4,5,6,7,8, and 9) serotypes) with different
promoters and eGFP has been tested for quick expression of the target protein
by self-complementary type. AAV-DJ-V65-eGFP shows the best expression
in 2~3 days (typical AAV expression takes about 3 weeks).
AAV9-CaMKIIa-GCaMP6s, AAV5-CAG-GCaMP6s, and AAV9-CAG-
GCaMP6f showed the best expression among different serotypes and these
are used for calcium imaging in zebra finches.
Figure 4.1. Virus expression results in juveniles (45-60 dph). Left top: AAV-DJ-V65_eGFP 3 days post injection. Right top: AAV5-CAG-GCaMP6s: >80% cases, good expression result in 20 days post injection. Left bottom: AAV9-CAG-GCaMP6f: > 70% cases, good expression result in 20 days post injection. Right bottom: AAV9-CaMKIIa-GCaMP6s: >67% cases, good expression result in 15 days post injection.
4.4. Discussion We found at least 3 GECIs (AAV5-CAG-GCaMP6s, AAV9-CAG-GCaMP6f, and
AAV9-CaMKIIa-GCaMP6s) successfully expressed in nucleus of neurons. In our
single- and two- photon imaging, those three virus combinations were generally
used. Even in mice, not many studies report successful expression from using of
GECI proteins (T.W. Chen et al., 2013; Georg B. Keller et al., 2012; Lütcke &
Helmchen, 2011). Even though many songbird groups has been shown that using
viral vectors can result successful delivery of target fluorescent proteins in birds
(Oberti et al., 2011; Roberts, Gobes, Murugan, Ölveczky, & Mooney, 2012), know-
hows and strategies are not well established as much as in mice. From our
combinational study of different viral vectors and promoters, songbird researchers
could potentially save their time to find out right combination for delivering target
GECIs to the target neurons.
5. Welfare of zebra finches during two-photon
imaging is investigated
5.1. Introduction
For last decades, a broad variety of neural recording devices have been developed
and have contributed to our current understanding in relations of brain functions
and behaviors. Remarkably, two-photon laser scanning microscope has shown
outstanding performance in recording single cell level neural activity for hundreds
of neurons in long-term (up to ~months) duration (Crowe & Ellis-Davies, 2014; Li,
Liu, Jiang, Lee, & Tang, 2017; Sadakane et al., 2015). Conventional
microelectrode technique has to be invasively inserted into cortical area and fixed
in the intact area permanently (Bjornsson et al., 2006; Woolley, Desai, & Otto,
2013). Therefore, it has restricted recording to its size (1 ~ 5mm) and results
deterioration of neural signal in long recording period. In contrast, two-photon
microscope does not require invasive insertion into intra-cortical area but records
fluorescent change induced by neural activity where cranial window locates (can
be > 5mm). Thus it has less spatial restriction than the other electrophysiological
recording devices, and it is more suitable to understand long-term behavioral
change such as motor learning with wider field of neural network dynamics (Crowe
& Ellis-Davies, 2014; Lütcke & Helmchen, 2011; Stosiek, Garaschuk, Holthoff, &
Konnerth, 2003b). This is why we want to use two-photon microscope for our
research to record populational neural activity in juvenile during vocal learning
period (>2 months).
Due to the fact that even several micrometer movements can result blurry imaging
during two-photon laser scanning, animal has to be head-fixed during brain
imaging. To let the animal be freely moving during imaging, miniaturized two-
photon imaging system has been attempt in rodents but it is not yet suitable for
bird's head because of the heavy weight of the devices (>3g) (Flusberg, Jung,
Cocker, Anderson, & Schnitzer, 2005; Fritjof Helmchen, Denk, & Kerr, 2013).
Instead, to avoid permanent head-fixation of the animal to the two-photon imaging
stage, freely mountable head plates have been developed and improved life quality
of the subjects.
Even though many efforts have been applied to release the burden of head-fixation,
it is not easy to examine stress level of the subject under pressure. Here we attempt
to estimate the stress level of head-fixed juvenile during 2 weeks of imaging period
by measuring body mass and song production change.
5.2. Method
5.2.1. Preparations for safe Head-fixation
Compare to the conventional heavy head plate used in rodents (> 2g), we had to
customize our head plate for birds, weighs less than 0.5g. Given the fact that
African women carry 20% body weights on their head without increased metabolic
rate after training (Maloiy, Heglund, Prager, Cavagna, & Taylor, 1986), we expect
that head plate weights 3% of the body mass can be adaptable to our birds by
training as well. Some animals are head-fixed (imaging) while awake for up to 40
times (over weeks) and during maximally two hours per day. In order to
reduce/avoid excessive escape reactions and injuries, the fixation is carried out in
a restraining jacket.
5.2.2. Lighting in two-photon chamber without damaging to photon
detector
Two-photon imaging requires highly sensitive photon detection from the excited
fluorophores in active neurons. In order to protect the photon detector from the
damage induced by too much light exposure, we have to maintain the two-photon
chamber in a very dark condition during imaging. However, we found that the
subject could fall asleep during imaging in a dark chamber.
We had looked for a solution to light up the chamber not to let the animal sleep
during imaging. Recently we found a solution to protect the photon detector from
damaging from bright light exposure by sealing the photon detector with light
absorbing dark materials. In addition, social companions like tutor or a female bird
has been accompanied next to the subject (<20 cm). We successfully managed the
subject don’t fall asleep during imaging and have a visual interaction with the
accompanied birds during imaging. It is also expected that this condition would
bring a positive effect if we want to tutor a juvenile during head-fixed imaging
because social interaction helps vocal learning (Y. Chen et al., 2016).
5.2.3. Optimizing imaging duration for the well-being of subject
Neural population development happens by rather rapid (in several hours) manner
(Roberts et al., 2010b). In order to record the neural ensemble organization to
various auditory inputs during vocal learning, the best condition to acquire the
appropriate data is to record neural activity in premotor area for the entire day and
daily from 25 dph (days post hatching) to 90~100 dph (crystallization of the song).
However, we understand that the subject has to sleep, eat, sing, and rest every day
for the subject’s well-being. Therefore, we compromised our imaging duration to
1.5 hour. The 1.5-hour session consists of 3 to 9 runs, and each runs consists of
various auditory inputs for 10 minutes. In some case, we perform the imaging every
other day during vocal learning period, then the animal can have more rest. With
these conditions, we reduce the number of restraints for experiment but still achieve
the data required to record developmental change of neural firing during vocal
learning period. This is the minimal condition required for our experimental criteria.
In addition, for the well-being of the subject, we also provide social companies
around him during imaging. This would help the subject has less corticosterone
level than alone in the chamber (Banerjee & Adkins-regan, 2011). During restraint,
the subject cannot drink water when needed. We provide water with a dropper near
the beak to let the subject stay hydrated.
In summary, to reduce the stress of the subject, 1) restraining is limited to 1.5 hours
per day for up to 40 days in time of 80 days covering the entire vocal learning
period, 2) social companies like tutor or female bird are accompanied, and 3) water
is supplied for the subject to maintain hydration during imaging.
5.2.4. Daily condition check by body mass and no. of vocalization
measuring
We suggest that body mass and no. of vocalization would indicate the condition of
the animal, and we could let the animals get accustomed and habituated to the head-
fixation by training. We found out the animals vocalize less than normal after
surgery or during early (first 3 days) head-fixed period (Fig 1B). However, several
days after daily 2hrs. of head-fixation, the animal increased the no. of vocalization
compare to the early days after surgery or first head-fixation. In addition, the body
mass of the head-fixed juveniles was within the range of the normal body mass
from 18 birds in the colony during and post head-fixation period (Fig 5A).
According to the paper from Picardo et al., 2016, it is also possible to let large
portion of birds sing (78/109) during restraining by training and reward (Picardo et
al., 2016).
Figure 5.1: Behavioral change by head-fixation. (A) Body mass of
juveniles during (left) and post (right) head-fixed imaging period. Box and
whisker diagram represents first (bottom of the box) and third (top of the box)
quartile, median (the line in the center of the box), and max. (Top whisker)
and min. (bottom whisker) of 18 juveniles (brood by father and mother, not
used for experiment, age range of 43~76 dph). b3o14 and b3o15 were head-
fixed for 11 days (2hr./day) before the body mass measurement and not head-
fixed during the 11 days of body mass measuring period. b3p1 was head-
fixed daily for 2 hours during the 11 days body mass measuring period. All
these three birds were song isolated from father at 15 dph, brood by mother
from 15-35 dph, and isolated individually in sound proof chamber from
35dph until the end of the experiment (115 dph). (B) No. of vocalization of
juvenile before and during head fixed imaging period (1hr in the morning/1hr
in the evening per each day). Each point is average of 3 days at the same
condition. Error bar shows standard deviation of measured values of the 3
days.
5.3. Discussion 2 hours of daily head-fixation during two-photon imaging induced high stress level in
the beginning of the two weeks of imaging period. This is shown by slightly dropped
body mass (~0.1 g, figure 5.1a) and song production amount in 6-8 days post the first
imaging day (Figure5.1b). However, the gradually increased body mass during the
imaging period shows that the juveniles adapted to the harsh condition by training. In
addition, song production amount was also increased in all birds (n=3). As Pircardo et
al. showed that head-fixed birds can sing during two-photon imaging after training
(Picardo et al., 2016a), we believe that we can reduce the stress level of juvenile by
daily habituation of the head-fixation.
Appendix A. Individual bird’s correlation between similarity change and singing duration was
inspected and its Pearson correlation has been computed.
(a) (b)
(c) (d)
0 1000 2000 3000 4000 5000 6000ï20
ï10
0
10
20
30
40
50
prev day song duration (s)
Cha
nge
in s
imila
rity
b8r17 r: ï0.589 p: 0.004
0 1000 2000 3000 4000ï15
ï10
ï5
0
5
10
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25
prev day song duration (s)
Cha
nge
in s
imila
rity
b7r16 r: 0.435 p: 0.048
0 1000 2000 3000 4000 5000ï15
ï10
ï5
0
5
10
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prev day song duration (s)
Cha
nge
in s
imila
rity
b13r16 r: 0.569 p: 0.008
1000 2000 3000 4000 5000ï15
ï10
ï5
0
5
10
15
prev day song duration (s)
Cha
nge
in s
imila
rity
b14r16 r: 0.241 p: 0.291
(e) (f)
(g) (h)
(i) (j)
0 1000 2000 3000 4000 5000ï15
ï10
ï5
0
5
10
15
20
prev day song duration (s)
Cha
nge
in s
imila
rity
r15y5 r: ï0.108 p: 0.648
0 1000 2000 3000 4000ï15
ï10
ï5
0
5
10
15
prev day song duration (s)C
hang
e in
sim
ilarit
y
r15y2 r: 0.171 p: 0.456
0 500 1000 1500 2000 2500 3000ï10
ï5
0
5
10
15
prev day song duration (s)
Cha
nge
in s
imila
rity
p3r16 r: 0.474 p: 0.034
500 1000 1500 2000 2500 3000ï15
ï10
ï5
0
5
10
15
20
prev day song duration (s)
Cha
nge
in s
imila
rity
g20r15 r: 0.412 p: 0.089
0 500 1000 1500ï8
ï6
ï4
ï2
0
2
4
6
prev day song duration (s)
Cha
nge
in s
imila
rity
g19r15 r: ï0.055 p: 0.815
0 1000 2000 3000 4000ï2
0
2
4
6
8
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Cha
nge
in s
imila
rity
p20r16 r: ï0.809 p: 0.004
(k) (l)
(m) (n)
Figure A.1. (a – n) Similarity change between days i-1 and i+1 versus singing duration on day i (with i ranging from the start day of recording from 38-48 dph to 60 dph, depending on the bird) reported for each individual bird. The Pearson correlation coefficients (r) are reported in each panel title, together with the p value and the bird’s name.
1000 2000 3000 4000 5000ï10
ï5
0
5
10
prev day song duration (s)
Cha
nge
in s
imila
rity
r15s12 r: 0.084 p: 0.748
0 1000 2000 3000 4000 5000ï15
ï10
ï5
0
5
10
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25
prev day song duration (s)C
hang
e in
sim
ilarit
y
k3r16 r: 0.104 p: 0.747
0 1000 2000 3000 4000ï20
ï15
ï10
ï5
0
5
10
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Cha
nge
in s
imila
rity
k6r16 r: ï0.248 p: 0.412
1000 1500 2000 2500 3000ï15
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imila
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b6r17 r: 0.39 p: 0.15
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