computing and movement coordinating cell groups in monkey motor cortex
DESCRIPTION
In our final project we would like to further explore the data from the laboratory of Dr John Donoghue which consists of electrophysiology measurements from the primary motor cortex (M1) of monkeys that performed center-out reaching task. The neural spikes were recorded using a 96- channel array that was chronically implanted into the primary motor cortexTRANSCRIPT
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COMPUTING AND MOVEMENT COORDINATING CELL GROUPS
MULTI-ELECTRODE MOTOR CORTEX DATA In our final project we would like to further explore the data from the laboratory of Dr John Donoghue which consists
of electrophysiology measurements from the primary motor cortex (M1) of monkeys that performed center-out
reaching task. The neural spikes were recorded using a 96- channel array that was chronically implanted into the
primary motor cortex (Figure 1).
The center- out reaching task the monkeys performed
consisted of three subtasks. Firstly, the subject had to
touch the center of the screen and hold its hand there.
Once the center was acquired, the monkey had to wait for
the onset of the target. This target then appeared in one of
the eight directions [0, 45, 90, 135, 180, 225, 270, or 315
degrees]. This direction is stored in the trial_angle
variable for each trial. Once another cue, the go cue onset
has appeared, the monkey had to move its hand to reach
the target in order to get a reward. The time of go cue
onset, as well as the time of starting the movement, and
the time of reaching the target (target acquired) have been
recorded and stored respectively in trial_go, trial_move
and trial_acq for each trial. The whole process is shown
on Figure 2 in the context of a similar experiment (Rao
& Donoghue 2014).
Figure 1 Placement of multi-electrode array (MEA), and location of M1 (Rao & Donoghue 2014)
Figure 2 The process of the cue-reaching task as described by Rao et al (Rao & Donoghue 2014). On this figure, the target is purple when not acquired, but
changes to yellow when the hand is in the acceptable (acquired) zone.
The data set includes 17 correctly executed trials for each
direction.
The electrophysiology data obtained from the MEA
includes the neural spike data of 62 neurons, some of
which are obtained from the same electrode. The actual
channel placement (channel_map) is also provided in a
10*10 array. Within the electrophysiology data set, a list
of arrays (spk_times) of spike times for the 62 neurons is
stored. In addition to that, the spk_channels variable
contains the actual channel from which the spike data
were taken from for each neuron. The average spike
waveform is also given for each channel in
spk_templates, but the individual spike waves are not
provided.
OUR EXPERIMENT
PART I: DECODING DIRECTION
In our project, the approach we took that was new to us
was to use a machine learning algorithm in Python to
learn to decode direction of movement from the neuron
activity patterns. A machine learning algorithm allows us
to make no assumptions about the manner in which
neurons in the motor cortex represent information about
movement. In other words, we seek to predict direction,
but do not specify how such direction is represented by
the neurons in the form of firing rates. As will be
described later, we look at firing rates during 2 different
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temporal phases (before AND after movement). This
makes using machine learning particularly relevant
because although it is understood that populations of
neurons encode movement direction by vectorial
summation of firing rates, the way primary motor cortical
neurons represent direction before movement (in a
possible cue-to-action processing stage) is not as well
described (Rao & Donoghue, 2014); thus, machine
learning allows us to make no assumptions on that end.
With that, we allow the algorithm to discover the rules for
such classification — in machine learning terms, this is
called “feature detection”, extracted from noisy data
which can contain irrelevant information that does not
contribute to the classification. These features are then
used as rules for subsequent prediction.
We implemented a machine learning classification
method from scikit-learn using a simple randomised
decision tree algorithm (Pedregosa et al., 2011). First, we
took the firing rates of neurons across trials of all
directions from the moment of cue presentation (trial_go)
to target acquisition (trial_acq). The goal for the
algorithm is to learn to predict direction of movement
when only information on the firing rates of neurons is
provided. Prediction accuracy determines how good the
classification is and this is measured by the “leave-one-
out” cross-validation method. Figure 3 depicts an
example (with made-up values) of how this works. Since
we have 136 trials/experiments, we train the algorithm
with data from 135 (136 - 1; N-1) trials, and leave one out
(trial 3, in the case depicted in Figure 3) to serve as a test
set. In this “unknown” test set, we provide the trained
algorithm with only the firing rates of all neurons, but
conceal the direction of movement that they were
originally recorded for. Based on its training on the other
135 trials, it predicts the direction associated with trial 3
(in our example). We do this 136 (N) times, once for
every trial and obtain a prediction accuracy determined
by the percentage of correct predictions over total
experiments (136). This method was chosen as we have a
small data-set and the method will provide us with greater
accuracy and stability. Using this method, we found that
using overall firing rates (from cue presentation to target
acquisition) gives 83% decoding accuracy.
Figure 3: Illustration of the leave-one-out cross-validation method. Given N trials in our data set, we perform N experiments. In each, N-1 examples
were used for training and 1 remaining left out to use for testing. In the test case, when provided only the firing rates of neurons, the algorithm predicts
the direction originally associated with the firing. The prediction is cross-validated with the known direction. This leave-one-out cross-validation is
performed N times and the total number of correct predictions out of N experiments is taken as a percentage to give prediction accuracy.
ARE ALL 62 NEURONS NECESSARY FOR DECODING
ACCURACY?
Next, we asked whether all 62 neurons in this data-set
were necessary to obtain this accuracy or was there some
level of redundancy. To address this question, we aimed
to reduce dimensions — that is, to estimate the
importance of neurons in providing classification
accuracy and reduce the set to include just the most
important neurons for classification. We estimated the
importance of neurons using the randomized decision
trees in scikit-learn as above (Pedregosa et al., 2011), and
identified neurons that contain the most information
necessary to predict direction (and for the classifier to
distinguish between directions). We found that training
the algorithm with a set of reduced dimensions — only 23
of the highest ranked neurons — was sufficient to achieve
83% accuracy. If every neuron contributes equally to
specifying direction, one would expect a steep drop in
predictive value with the loss of as many as 64% (23/62)
of the neuronal population! This suggests that these 23
neurons had firing rate patterns that were sufficient to
decode direction.
Interestingly, when 23 units were randomly chosen to
train the algorithm, it achieved a predictive score of 63 ±
8% (mean ± standard deviation, 10 runs). This implies
that not just any 23 neurons are sufficient for prediction
— the neurons identified to have strong predictive power
are indeed very likely to contain important features to
decode direction. Do neurons other than the 23
“important” ones contain information that is 1) redundant
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2) not important for direction specification or 3) do they
encode other information related to direction but are not
represented in the form of firing rates?
ARE THE 23 HIGHEST RANKED NEURONS PREDICTIVE
BECAUSE THEY CONTAIN UNIQUE, NON-REDUNDANT
FEATURES? To understand if the population contains neurons that
specified for directions with redundancy, we did a
correlation analysis. This is achieved by taking the first
37 seconds of recording with neither visual stimulus nor
motor activity, which can be regarded as the baseline, and
then performing a spike cross-correlation. This allowed
us to identify neurons with correlated activity (without
the influence of any stimulus), following the Hebbian
principle that neurons that wire together, fire together --
and may thus represent redundant directional tuning, such
that loss of redundant neurons do not affect the predictive
power of the population, as described above.
We then asked if these 23 neurons were sufficiently
predictive of direction because they contained unique &
non-redundant direction-specific information. If so,
extracting 23 unique neurons with the least mean
correlation coefficients - that is, “most unlike” other
neurons recorded in that array - and testing the directional
decoding accuracy of this subset should yield comparable
predictive score (83%) as the 23 “most direction-
predictive” neurons. Surprisingly, taking 23 most
uncorrelated neurons instead yielded a predictive score of
71%, less than expected. This suggests that amongst the
23 sufficiently direction-predictive neurons described
earlier, there are also neurons that contain partially
redundant information. As such, whether a neuron
contributes largely to direction specification is not
strongly associated with whether it contains unique, non-
redundant directional information.
PART 2: Following these preliminary & exploratory findings, we
hypothesized that the response properties of neurons in
the motor cortex are heterogeneous. As such, although
the firing rate of all neurons in general contains some
direction-specific information, it is possible that its
overall activity also contains information that contributes
to generating movement direction in different ways. For
example, the activity can also specify responses
associated with the visual cue and/or movement planning
prior to the movement. Although such functions are
conventionally thought to be subserved by the premotor
cortex and supplementary motor cortex, there is
increasing evidence that neurons in the primary motor
cortex can also carry information more than direction of
movement (eg. Rao & Donoghue, 2014). There is also
evidence that the directional tuning of neurons is context-
dependent, with factors such as somatosensory input
about limb position and visual cues modulating the
directional tuning (eg. Churchland & Shenoy, 2007).
Interestingly, motor cortical neurons have also been
shown to respond with directional preference with respect
to different reference frames (some with “intrinsic-space”
preferred direction with reference to subject’s arm, and
some with an “extrinsic-space” preferred direction with
external space as a reference) (Kakei, Hoffman & Strick,
1999). In the case of our data set, the directions for the
trials are all specified in “extrinsic space”.
CAN NEURONS BE CLASSIFIED BASED ON DIFFERENT
RESPONSE PROPERTIES BEFORE AND DURING
MOVEMENT? This question motivated us to explore the data and
attempt to classify neurons based on their response
properties. We noticed that neurons fired not only during
movement but also before the actual movement,
suggesting a processing phase. To understand how these
different time periods of firing contribute to movement
direction, we considered 1) firing between go cue onset
(trial_go, in dataset) and the start of movement
(trial_move) as activity that may be associated with
motor planning or cue-to-action processing; and 2) firing
between movement (trial_move) and target acquisition
(trial_acq) as activity associated with the generation of
motor output.
Within each of these two different time windows, we ran
the same algorithm (as in part 1) to identify the neurons,
based on their characteristic firing rate, which are most
important for conveying directional information. This
gives us the neurons whose firing, between go cue onset
to start of movement AND movement onset to target
acquisition, made them likely to be involved in specifying
direction through cue-to-action processing and motor
output respectively. With that, we are also able to identify
neurons which are important for specifying direction in
both of these temporal categories. This gives us 3
classifications of neurons, as illustrated in the form of
the Utah array in Figure 4: neurons important for
specifying direction during 1) cue-to-action processing
(red), 2) motor output (blue) and 3) both of these
functions (purple shades between red & blue showing
relative importance).
From Figure 4, we can make a few broad observations.
Firstly, cue-to-action processing neurons (red) were not
specifically clustered at a particular area in the electrode
array, which could suggest potential encroachment of the
premotor cortex, which carries cue-related information.
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Instead, there is no clear pattern, with these neurons
seemingly interspersed across the array.
Figure 4: Spatial organization of neurons important for specifying
direction during cue-to-action processing (red), motor output (blue) and
both of these functions (purple shades between red & blue showing
relative importance of the phase). The size of each circle represents the
neuron’s importance in decoding directions through its overall activity
from cue presentation (trial_go) to target acquisition (trial_acq).
In addition, there was no clear spatial cluster of neurons,
which appear to represent directions through both
functions (purple). This was likewise observed by Rao &
Donoghue (2014) with a slightly different methodology.
The neuron’s relative importance (size of circles) also
does not appear to have a defined spatial organization.
Secondly, the largest proportion of neurons is active
through both of these processes (purple), although to
varying extents. Thirdly, there is no correlation between a
neuron’s importance in decoding direction during the
whole duration (cue to target acquisition; depicted as size
of circles) and whether it is specifically important in
either functions (cue-to-action processing or motor
generation). As such, neurons whose activity is important
for decoding direction with overall activity from cue to
target acquisition spread across the 3 categories of
neurons that we identified earlier.
Next, we decided to explore if activity during only cue-
to-action processing or only motor output was sufficient
to give the prediction accuracy obtained with overall
activity. When we trained the algorithm using firing rates
of all neurons before movement (cue-to-action
processing), we found that it was sufficient to predict
direction with 67% accuracy, while training only with
the highest ranked 23 neurons achieved marginal
increase to 68%. It is worth noting that this accuracy is
slightly higher than when considering overall activity of
23 randomly chosen neurons! This suggests that even
considering activity only before movement can achieve
reasonable accuracy to decode direction, suggesting that
this phase likely contains some important direction-
specific information, possibly planning processes.
Training the algorithm using firing rates of all neurons
during the motor output led to a prediction accuracy of
72% while considering only the 23 highest ranked
neurons for motor output achieved greater accuracy of
76%. Taken together, neither activity during cue-to-action
processing nor during movement were sufficient on their
own to achieve the 83% accuracy obtained from overall
activity, implying that activity during both phases
make important contributions to improving the
accuracy of decoding direction. These results are
summarized in Figure 5.
Finally, in Figure 6, we provide tuning curves for 6
representative neurons to illustrate the types of neurons
we have observed in this data-set. We found some
neurons (Figures 6a & 6b) that have rather different
tuning curves during the cue-to-processing & movement
phases, with only partial overlap during these phases. In
these neurons, both breadth of tuning and levels of
activity appear to be different during cue-to-action
processing and active movement. For example, the
neuron shown in Figure 6b has a sharper tuning,
particularly for the 315° direction during active
movement (blue - - -). In contrast, Figures 6c to 6f show
neurons that have comparable tuning curves across
directions during cue-to-action processing and motor
generation. However, whereas neurons shown in Figure
6c and 6d have different levels of activity during the two
phases, neurons shown in Figures 6e & 6f have similar
levels of activity. In the case of the neuron shown in
Figure 6d, there is greater activity and it also appears to
be more broadly tuned during the cue-to-action
processing phase.
Figure 5: Percentage prediction accuracy of neurons in the dataset when overall activity, only cue-to-action processing activity or only motor output-
associated activity were considered.
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PROBLEM THAT REMAINS TO BE SOLVED
IS THE MONKEY GETTING ANY BETTER? According to the study by Kargo & Nitz (Kargo & Nitz
2003) performed on rats trained to do a reach-to-grasp
task, learning has at least two phases. They have shown
that in the early phase, on the first day of training, the
performance of the subject animals showed fast, within
day improvements. In another study by Xiao (Xiao 2005),
monkeys performed a visually guided reaching task using
a robot arm. The main conclusion of this paper was that
premotor cortex neurons show learning-related plasticity.
It has also been pointed out that memory can be displayed
by cell activity properties, such as average firing rate,
dynamic range or preferred direction.
Although in the provided dataset we have no information
about the duration of the breaks between the successful
trials, nor do we know whether the full experiment was
performed on a single day, but it is reasonable to assume
that the trials are given in the order they were performed.
Based on these assumptions, our second question was to
determine whether the monkey is getting better at the
task. In order to quantify that process, we considered the
changes in reaction time. We also hypothesized a
correlation between the average firing rate, supported by
Xiao’s findings (Xiao 2005), and we aimed to determine
and whether this change of activity if there’s any is
different for the previously identified cell groups.
Preliminary exploration of the provided data did not show
any of the hypothesized correlations, but the amount of
data analyzed is not sufficient to draw any conclusions.
We have concluded that there must have been a
preliminary training period before the experiments that
we have the data from. Improvement in performing the
task could not have been observed, inasmuch as the main
learning period was over. In order to address the proposed
question, more extensive analyses on data from the
training period should be carried out separating different
directions.
FUTURE QUESTIONS We used a machine learning algorithm for our
classification. However, this algorithm is limited by its
ability to only predict directions in the 45 degree intervals
(0, 45, 90 etc) as in this dataset and cannot predict
intermediate directions, as would be achievable by an
algorithm which implements vector summation. Thus, it
may not be generalizable to other data-sets where
directions of movement tested are different from that used
in our data. Our method is a fast and simple way to
understand whether activity after cue presentation and
prior to movement could have important implications on
predicting direction. Following our results, it will be interesting to implement an algorithm capable of
decoding intermediate directions of movement. However,
in order to do so and obtain a reasonable accuracy, a
larger data-set containing more neurons will be necessary
to train the algorithm to decode directions.
As it has previously been proposed, the primary motor
cortex is not only involved in action generation, but
neuronal networks may also perform cue-to-action
processing related computations. Multielectrode array
recordings in awake Macaca monkeys have shown that
MI activity is partially related to visual target processing,
i.e. certain cells of the motor cortex are relevant in this
process. These results suggest that MI is an integral part
of a cue-to action network that responds to stimuli (Rao
& Donoghue 2014). In their analysis, Rao et al have used
cue or movement alignment to determine whether
responses were related more to cues or movements. The
task performed by the monkeys consisted of manual step
tracking and pursuit tracking. The step tracking
enabled the authors to separate the relationship between
cues and movements, while the pursuit tracking
investigated the influence of the target.
During the manual step tracking task, the monkeys had to
acquire the center of the screen and hold for 1000-2000
ms. Afterwards, for the pursuit tracking task, a C1 target
has appeared at the periphery indicating the direction for
an impending movement. When C1 and the first center
target have simultaneously disappeared, a new target, C2
has appeared at the same location as C1 did previously. In
this second part of the experiment the monkeys were
required to reach C2 and hold for 1000-1500 ms, and then
they were rewarded. By distinguishing between C1 with
no movement and C2 with movement, the authors were
able to separate the activity that is cue related only from
movement-related only activity.
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Figure 6a-f: Tuning curves for representative neurons during cue-to-action processing and move phases. See description in text.
In our case, we identified cue-to-action processing
activity as all activity before a movement, which is a
rough approximation — indeed we cannot exclude the
possibility that we may not have isolated purely cue-to-
action processing activity using such a method. In future
explorations of potential roles for cue-to-action
processing activity, it will be important to rigorously
isolate such activity using experimental paradigms like
that applied by Rao & Donoghue (2014). By isolating
purely cue-to-action processing activity, we may then be
able to more rigorously examine how neurons represent
direction of movement through such activity.
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