Zbornik 9. mednarodne multikonference Proceeding of the 9th International Multiconference

INFORMACIJSKA DRUŽBA IS 2006

INFORMATION SOCIETY IS 2006

Kognitivne znanosti

Cognitive Sciences

Uredili / Edited by

Urban Kordeš, Matjaž Gams, Olga Markiÿ

http://is.ijs.si

10. oktober 2006 / 10th October 2006 Ljubljana, Slovenia

KAZALO / TABLE OF CONTENTS Cognitive Sciences.................................................................................................................................................313 PREDGOVOR .....................................................................................................................................................315

Reflections on Reflection – Assumptions and Analogies / Bojadžiev Damjan...................................................317 From cortical networks oscillations to behaviour: memory in neurophysiological perspective / Brežan Simon..............................................................................................................................................................321 Pasti v raziskovanju fenomena zavesti / Detela Andrej......................................................................................328 The Nature of Strategy Process: A Psychological Interpretation / Dolenšek Jernej ..........................................330 Daniel Dennett in raÿunski obrat / Gams Matjaž ................................................................................................334

Refleksija raziskovanja prepoznavanja povezanosti med biografskim potekom in odloÿitvijo za poklic pomoÿi / Klemenÿiÿ Marija Mija.....................................................................................................................338 Nekateri metodološki in epistemološki kognitivizma / Knap Žiga.......................................................................341 Temeljni koncepti psihoanalize in njihov vizualni ekvivalent: Lacan skozi holografijo – uvod / Koltaj Pavel .....343 Poroÿilo o okrogli mizi »Multidisciplinarno o spominu« / Kordeš Urban, ÿernigoj Matej, Vodušek Vid, Bohanec Marko, Beliÿ Aleš, Brežan Simon, Štukovnik Vita...........................................................................348 Ustvarjanje prostora za prvoosebno raziskovanje – 1. del: razlogi / Kordeš Urban...........................................354 Ustvarjanje prostora za prvoosebno raziskovanje – 2. del: izvedba / ÿernigoj Matej ........................................357 Utemeljitev biološke inteligence kot elementarne kognitivne lastnosti vseh organizmov / Krašovec Rok, Jerman Igor ....................................................................................................................................................359 Je elementarna kognitivna lastnost imenovana biološka inteligenca zakonitost ali nakljuÿje? / Krašovec Rok, Jerman Igor ............................................................................................................................................363 Iskanje integrativne metode – primer raÿunalniške metafore / Markiÿ Olga ......................................................366 Evaluation of the semantic similarity of the words denoting emotions / Martinovska Cveta..............................370 Zavest in psihoterapija / Možina Miran...............................................................................................................374 Realna moÿ zavesti / Škarja Metod, Jerman Igor, Leskovar T. Robert .............................................................378 Alternativa "monadiÿni" paradigmi kognicije: Koncept socialno porazdeljene kognicije / Ule Andrej ................382

From cortical networks oscillations to behaviour: memory in neurophysiological perspective

Simon Brežan Faculty of medicine, University of Ljubljana

SI-1000 Ljubljana e-mail: s_brezan@ yahoo.com

ABSTRACT

Cognitive neuroscience searches for neuronal correlates of higher mental functions, including working memory. Various neuropsychological phenomena and models are commonly explored by two basic approaches: functional imaging studies (fMRI, PET, etc.) and electrophysiological methods (electroencephalography- EEG, intra/extracellular electric potentials detection, etc.). High spatial resolution of functional imaging methods allows precise neuroanatomical identification of activated neurons during the performance of specific cognitive tasks. On the other hand, with electrophysiological methods the same phenomena can be explored on the level of electrical activity of the brain, therefore with better time resolution (milliseconds). This allows the study of basic mechanisms of information processing (including working memory) and operational system of the brain. EEG-coherence, as a newer method of EEG signal analysis, is suitable for studying integrative brain function. It measures interregional synchronized oscillatory activity

of neurons as one possible mechanism of the functional integration – binding of different communicating brain areas, present with different distributed brain processes and behavioral modes, for example working memory.

1 INTRODUCTION 1.1 WORKING MEMORY (WM) Memory includes the following main processes: encoding (cognitive events during presentation of information), storage (maintenance) and retrieval (recall) of information. Its basic structure can be described by different memory stores (sensory memory, short-term and long-term memory), each with specific structural and processing characteristics.

Zbornik 9. mednarodne multikonference Proceedings of the 9th International Multiconference

INFORMACIJSKA DRUŽBA IS 2006 , INFORMATION SOCIETY IS 2006 http://is.ijs.si

9. do 14. oktober 2006 / 9th - 14th October 2006

Ljubljana, Slovenia

Figure 1. Basic structure of memory (Atkinson and Schiffrin, 1968). The short-term memory is also termed working memory. This emphasizes its active role in cognitive processes – besides temporal storage of information, the information is also available for active manipulation. WM can therefore be defined as a complex of cognitive processes for maintenance, manipulation and utilization of mental representations. One of the best studied models of WM is Baddeley’s model. It consists of the attentional control system – the central executive (CE), and 3 subsystems: the phonological loop, the visuospatial sketchpad and the episodic buffer. CE plays a major role in mediating many cognitive processes and is assumed to resemble the supervisory attentional system (SAS model, Norman and Shallice, 1986). It is a limited capacity control system that is responsible for controlling other subsystems, for strategy selection and planning. Information is stored and actively rehearsed or updated in the modal specific subsystems – visuospatial information in visuospatial sketchpad and verbal information in phonological loop. Also the conative, emotional and motivational control of WM and its role in multi-level control of action (Frith et al., 2000) is crucial, but largely ignored. What drives the System?

replacement loss interference

attention Sensory memory

Short-term memory

Long-term memory

rehearsal

Central executive

Visuospatial sketchpad

Episodic buffer

Phonological loop

Visual semantics Language Episodic long

term memory

Figure 2. Baddeley’s working memory model (Baddeley, 2000). 1.2 NEUROPHYSIOLOGICAL AND NEUROANATOMICAL BASIS OF WORKING MEMORY The basic neurophysiological mechanism of short-term (working) memory is supposed to be based on short-term increase of synaptic efficacy in specific neural network pattern and on repeated reverberations of electrical impulses in reverberational (feedback) loops (Štrucl, 1999). The repeated excitation (by depolarization by external incoming stimuli) of a synapse in such excitational loop leads to a progressive increase of excitatory postsynaptic potential (EPSP).

This mechanism of short-term postsynaptic facilitation can drive the time-limited maintenance/ reverberation of specific information, coded by a specific sustained activation of neural network pattern. Here, functional-only plastic change (increase) in synaptic transmission efficacy (increased synaptic weight) is based on the presynaptic depolarization and/or cAMP/protein kinase activation and includes calcium-dependent mechanisms of sustained increase of neurotransmitter mobilization/release even after the relevant external stimulus is not present anymore. The process of active rehearsal, repeating the information in mind, which further maintains the information in working memory, could hypothetically enhance or even induce the proposed electrophysiological changes. Dilemmas persist also about how information is stored or manipulated in working memory (reverberational excitatory circuits, »delay-period activity« neurons) and what is the nature of information stored (retrospective vs. prospective codes). Various components of working memory are mediated by different anatomically separated neuronal networks. Research shows that WM is mainly based on the activity in prefrontal, premotor, limbic, and posterior association temporo-parietal areas of the brain. The exact mechanisms of dynamic interactions of many involved but anatomically separated brain regions with neural mechanisms for information processing itself are not yet well known. The functional integration (‘binding’) of different brain areas, responsible for specific (working memory) functions, is one of the key problems in understanding brain function – a question not yet resolved.

Binding is perhaps mediated by synchronized oscillatory activity of neuronal networks, which can be determined by the electroencephalographic (EEG) coherence analysis. Coherence between two EEG signals (x, y) equals the squared cross-correlation power spectrum in the frequency domain. It shows, how synchronously the power of certain frequency band changes between two distant brain areas over time and reflects the degree of phase-locking (coupling) between two distant signals as a measure for the degree of inter-regional synchronization of neural oscillations.

2 SYNCHRONOUS NEURAL OSCILLATIONS AND COGNITIVE PROCESSES EEG is a method, which measures repeated, periodic electrical activity of cortical neurons. Summed activity (mainly postsynaptic potentials with resulting extracellular ionic currents) of many neurons results in field potentials, many of them constituting macropotential (EEG signal). It is influenced by intrinsic qualities of neurons and by dynamic interactions between communicating neuronal networks. Macropotential is a result of changing pattern of synchronization and desynchronization of regional brain cells, as this results in amplitude changes of specific frequency bands. EEG has great time resolution and shows distinct patterns of activity (brain rhythms, oscillations).

Brain rhythms can be divided in many frequency bands (delta: 0,5-4 Hz, theta: 4-7 Hz, alpha: 8-13 Hz, beta: 13-30 Hz, gamma: more than 30 Hz) with their specific functional and behavioral correlates and activating contexts and spatial scales. The proposed role of brain oscillations is switching neural networks between different functional states with activating or inhibiting proper neural systems. Evidence about causality of this relationship is also based on some computational simulation neural models. The central problem for cognitive neuroscience is to describe how cognitive processes arise from the brain. Recent evidence shows that synchronous neural oscillations and computations performed by such assemblies of functionally connected neurons reveal much about the nature of cognitive processes such as (working) memory, attention and consciousness. Memory processes are most closely related to theta and gamma brain rhythms, whereas attention seems closely associated with alpha and gamma rhythms. Conscious awareness may arise from synchronous neural oscillations occurring globally throughout the brain rather than from the locally synchronous oscillations that occur when a sensory area encodes a stimulus. The emerging view of brain processes is based on reverberations of reentrant electrical activity in complex neural networks. Synchronization is how the brain probably achieves large-scale integration of its many parallel, distributed processing activities, allowing coherent cognition and behaviour. The goal is to describe how this oscillatory activity of the brain as revealed by the electroencephalography (EEG) or magnetoencephalography (MEG) as well as

more invasive recordings, is related to the dynamics of cognitive performance, including working memory. The neurophysiological theory of higher mental functions predicts that multiple superimposed synchronized (coherent) oscillations in different frequency bands with different spatial patterns and functional correlates govern specific mental functions. In working memory, mainly pre(frontal) and posterior association brain areas were found oscillating synchronously (theta, gamma, alpha band), which could mean that either prefrontal cortex itself rehearses and maintains the information transferred from posterior storage areas and manipulates with it or it inserts top-down control by its central executive function. Baddeley’s model of working memory predicts constant interplay and functional coupling between central executive control system (probably mediated by prefrontal cortex, Collette et al., 2002) and slave subsystems for storage of verbal and visuospatial information, located in posterior and premotor brain regions (Gathercole, 1999). 3 NEURAL ENCODING VS. DECODING The synaptic connections in brain networks play a very important role as their specific organization in a given brain area determines a specific function performed by that area. Researchers develop large-scale mathematical models of brain networks and then explicitly simulate these models using high-performance supercomputers.

Neural en-coding of information represents a systematic variation of neural activity with respect to a behavioural variable (e.g. stimulus-response function). Known research options are single-cell firing rate activity recordings etc. and standard statistics can be used. On the other hand, just the opposite, neural de-coding refers to extracting the value of specific variable from the neural activity, which makes it possible to predict behaviour. Here, we consider extracted data from wider neuronal populations and multivariant statistics must be implemented to study neural population vectors (NPV) for continuous variables or discriminant analysis for discrete variables. Simulations of large-scale brain models could be important for detecting the relevant brain signals and their spatial localization and temporal dynamics. MEG or EEG data signals carry sufficient trajectory information to predict/decode the movement trajectory or even higher mental and musical imagery just from these noninvasive recordings alone. So information is present in a signal and the challenge remains how to extract it. MEG (and EEG) from whole brain signals shows overlapping but still distinct, different dynamics for different tasks. The cross-correlation function (CCF/ coherence) between pre-whitened data adds to these studies by exploring the interactions among neuronal populations. NEURAL CORRELATES OF SERIAL ORDER AND MECHANISMS OF MEMORY SCANNING In studying memory, serial-order neural mechanisms are very important as everything in time is serially ordered and all processes are

meaningful only in time context of serial sets: the stream of thoughts, the flow of conscioussness, memory or any integrative action, speech etc. In memory serial-recall tasks (e.g., redrawing a geometric shape after presentation) we can observe a switch abrupt activity to different NPV vectors according to the serial position in a sequence without any spatial component included (Georgopoulos et al., 2005). In context-recall serial position memory scanning tasks it has been proved that cca. 20% of all brain cells activated during the specific task encode only the specific serial positions of stimuli, the others encode direction, force of movement etc. In single-cell recordings it can be revealed that a certain percentage of cells specifically activates/ fires as a function of serial position (which is shown in the frequency of its action potential firing rate), but others fire only with respect to specific segment, shape, length or movement parameters etc. regarding redrawing a shape. Parallel co-processing of more parameters is needed, as we use serial order movements to construct or copy/ redraw e.g. a certain shape. How then to decode a specific segment serial position? Analysis of internal neural representations shows separated strengths of the serial order position activities after the shape was shown and this separation of different population activites starts before and is also prolonged to the redrawing/copying of shape phase, with separate distinct peaks of firing of different involved neural ensembles for different time points/positions in the sequence/series (Carpenter et al., 1999)

NEUROPHYSIOLOGICAL WORKING MEMORY MODEL: LISMAN IDIART JENSEN (LIJ) MODEL Experimental work based on single cell recordings supports the hypothesis that working memory representations are retained by sustained neuronal firing. While this hypothesis can account for the maintenance of a single memory item, it remains unclear how multiple working memory items are represented with serial order retained. Here we will discuss the possible physiological mechanism responsible for the maintenance of multiple working memory items including mechanisms based on sustained firing and synaptic encoding. The model focuses on temporal segmentation by phase encoding, namely by the idea that several working memory items are activated sequentially at different points in time. It has been proposed that a mechanism of nested gamma (30–80Hz) and theta (4–8Hz) oscillations is responsible for controlling the reactivation of the memory list. This mechanism has been shown to be compatible with multiple behavioral findings on working memory such as the data from the Sternberg experiment. The model predicts that different memory representations (7 items ± 2) are encoded and activated separately and sequentially in subsequent phases of theta cycle. This means the model is based on serial information processing principles. Therefore it can explain empirical data (Sternberg, 1964) on the increase of reaction times with respect to the working memory load (number of items). The theta input serves as a signal carrier and induces repeating of scanning and working memory maintenance processes. The source of theta oscillations could

be the hippocampo-cortical reverberating loops.The memory code itself is mediated by synchronous gamma oscillations, induced by a pattern of alternating excitation and inhibition of different memory code population activities. Working memory processes are thereby potentially mediated by posterior (sensory)-hipocampal- prefrontal neural network, which is integrated and functionally coupled by the synchronization of oscillations in theta frequency band. Figure 3. LIJ model (1998) of a network functioning as a multi-item short-term memory buffer. Theta and gamma oscillations play an important role in the concept. Afterdepolarization (ADP) is triggered after a cell fires (sensory input) and it causes an increasing depolarizing ramp that serves to trigger the same cell to fire again after delay. These ramps are temporarily offset for different memories, an offset that causes different memories to fire in different gamma cycles.

The key function of this buffer is to perpetuate the firing of cells in a way that retains serial order. The repeat time is determined by theta oscillations due to the external input. Gamma oscillations arise from alternating global feedback inhibition and excitation (the cell with the most depolarized ramp will fire again) because of separate firing of different memory codes. In the retention interval (delay period), the items are maintained by activity-dependent intrinsic properties of the neurons coding these items (based on ADP- membrane potential changes which depend on distinct ion channels properties). CONCLUSIONS The described neurophysiological and neurocomputational approaches and research could bring further progress in understanding our brain and mind also with important applicative value, considering the development of new technologies of »neural prostheses« and brain-computer interface (BCI) implementation. These would equip us with necessary tools for »behaviour prediction and control«, including the possibilities of automation and control of different artificial computer-based processes and machines. Eventually, neuroscience may even succeed with the nowadays still more or less futuristic fantasies of human »mind-reading«. All that challenges and fundamental progress might possibly be accomplished just by intelligently taking into account the temporal and spatial dynamics of relevant electrochemical signaling in our brains, thereby discovering and revealing our miraculous »inner universe«.

REFERENCES 1. Babiloni C., Carducci F., Vecchio F., Rossi S., Babiloni F., Cincotti

F., Cola B., Miniussi C., Rossini P.M. (2004). Functional frontoparietal connectivity during short-term memory as revealed by high resolution EEG coherence analysis. Behavioral neurosciencies, 118(4), 687-697.

2. Baddeley, A. (2000). The episodic buffer: a new component of

working memory? Trends in cognitive science, 4(11), 417-423.

3. Basar E, Basar-Eroglu C, Karakas S, Schurmann M. Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int J Psychophysiol. 2001 Jan;39(2-3):241-8.

4. Buzsaki G, Draguhn A. Neuronal oscillations in cortical networks. Science 2004 Jun 25;304 (5679):1926-9.

5. Carpenter AF, Georgopoulos AP, Pellizzer G. Motor cortical encoding of serial order in a context-recall task. Science. 1999 Mar 12;283(5408):1752-7.

6. Collette, F., Van der Linden, M. (2002). Brain imaging of the central executive component of working memory. Neuroscience Behavioral Review, 26, 105-125.

7. Eysenck., M.V. in Keane, M.T. (1990). Cognitive psychology: A student's handbook. London: Erlsbaum Associates

8. Fuster JM. Cortical dynamics of memory. Int J Psychophysiol. 2000 Mar; 35(2-3):155-64.

9. Gathercole, S.E. (1999). Cognitive approaches to the development of short-term memory. Trends in cognitive science, 3(11), 410-419

10. Georgopoulos AP, Langheim FJ, Leuthold AC, Merkle AN. Magnetoencephalographic signals predict movement trajectory in space. Exp Brain Res. 2005 Nov;167(1):132-5

11. Jensen, O., Lisman, J.E. (1998). An oscillatory short-term memory buffer model can account for data on the sternberg task. The journal of neuroscience, 18(24), 10688-10699.

12. Jensen O. Maintenance of multiple working memory items by temporal segmentation. Neuroscience. 2006 Apr 28;139(1):237-49.

13. Kandell, E.R., Schwartz, J.H., Jessell, T.M. (2000). Principles of neural science. New York: McGraw-Hill.

14. Klimesch W. (1996). Memory processes, brain oscillations and EEG synchronization. Internal journal of psychophysiology, 24, 6-100.

15. Sarnthein, J., Petsche, H., Rappelsberger, P., Shaw, G.L., von Stein, A. (1998). Synchronization between prefrontal and posterior association cortex during human working memory. Neurobiology, 95, 7092-7096.