Supplementary Material for: Correlation of neuronal firing rate,
firing rate correlation, and synchrony in area MT with
directional choices during stimulus and reward expectation
Thiele, A., Hoffmann, K.-P.
Definition of early and late SIDs (and its influence on neuronal choice
probabilities)
The definition of early and late SIDs in the main paper was based on the maximum of
the cumulative hazard function plus 500 ms. The additional 500 ms were added
because we reasoned that the resulting cut-off more appropriately reflects the
internalized time of decisions in relation to the last possible stimulus presentation.
This reasoning is based on decision times in relation to low (2%) and medium (4%)
contrast stimulus presentation. Such decisions followed stimulus onset by ~350-500
ms 1. Although we believe that it is reasonable to assume that their internalization may
be based on the percept/decision/motor response time following these low contrast
stimuli, the animals might have had different mechanisms to determine and internalize
the maximum of the cumulative hazard function. To control for the possibility that the
cut-off used in the main paper was arbitrary and different cut-off points would
generate different results in relation to firing rate differences and choice probabilities,
we used a variety of different cut-off points. We calculated the choice probability
associated with early and late SIDs, whereby the maximum of the cumulative hazard
1
function divided the two types of SIDs (i.e. the maximum of the hazard function itself
was taken as the cut off point), or when a variety of different delays relative to the
cumulative hazard function maximum divided them (Supplementary table 1). As a
further control we defined early and late SIDs for each experimental session based on
the animals’ overall SID timing distribution during that session. Here SIDs were
subdivided into two groups of equal size, separated by the median SID time that
occurred in the specific experiment. The latter approach ensured equal numbers of
early and late SIDs in PD and APD direction, and also accounted for the fact that the
exact speed of SIDs varied somewhat between experimental sessions. The analysis of
these different cut-off times demonstrates that the finding reported in the main paper
is robust with respect to the exact choice of early and late SID definitions. The ROC
values in Supplementary Table 1 show that any cut-off from the maximum of the
cumulative hazard function up to 600 ms thereafter yields significantly larger ROC
values for early than for late SIDs. When cut-offs before the maximum of the
cumulative hazard function were chosen early SIDs started to contribute to the ‘late
SID’ ROCs, and the difference between the two groups decreased, cut off’s more than
600 ms after the maximum of the cumulative hazard function resulted in late SIDs
contributing to the ROC calculation for early SIDs, and the differences between the
ROC distributions decreased and became non-significant.
Definition of
SIDs (cut-off
between early
and late)
Cumulative
hazard function
maximum -200
ms
Cumulative
hazard function
maximum
Cumulative
ROC early SIDs
ROC late SIDs
(n=89)
p-value for
difference (signed
rank test)
Significantly
affected cells
(permutation
test)
n early: 11
n late: 9
0.541 +/-0.15
n=52
0.516 +/0.083
P=0.159
0.530 +/- 0.12
n=89
0.503 +/-0.089
n=89
P<0.043
n early: 15
n late: 15
0.549+/-0.122
0.503 +/-
P<0.001
n early: 27
2
hazard function
maximum +200
ms
Cumulative
hazard function
maximum +400
ms
Cumulative
hazard function
maximum +500
ms
Cumulative
hazard function
maximum +600
ms
Cumulative
hazard function
maximum +800
ms
Split according
to the median
SID time for
each session
n=124
0.085
n=124
n late: 13
0.542+/-0.115
n=135
0.504 +/0.088
n=135
P=0.003
n early: 26
n late: 14
0.546+/-0.107
n=138
0.507 +/0.082
n=138
P<0.001
N early: 29
N late: 19
0.542+/- 0.102
n=133
0.509 +/-0.086
n=133
P=0.015
N early: 28
N late: 14
0.534+/- 0.107
n=118
0.512 +/-0.099
n=118
P=0.101
N early: 25
N late: 8
0.541 +/- 0.101
n=164
0.504 +/0.083
n=164
P<0.001
N early:32
N late: 16
Supplementary Table 1: Effect of choosing different cut-off points to define early and late
SIDs on the choice probabilities. Early SIDs resulted in larger choice probability values for all
the cut-offs shown in the table. However, the difference was only significant for cut-off points
that started when the cumulative hazard function had maxed out until 600 ms thereafter. We
argue that the decrease with very early cut-offs occurs because then early SIDs trials
contaminate the late SID trials, while with later cut-offs late SID trials contaminate the early
SID trials.
SID timing and associated reward probabilities
It could be argued that pooling SIDs across different sessions may have confounded
the results in relation to the reward yield for early and late SIDs. This is because
animals might have indicated more early SIDs during the early part of each
experimental session (where they were more thirsty and possibly less patient, and also
were likely to have forgotten the choices from the previous session), while they would
indicate more late SIDs during the late part of the session, where they had better
3
memory regarding previous choices and thus reward probabilities, as well as having
reduced thirst levels. To control for this possibility we subdivided each session into
two equal halves, and analysed the time of choice for each of these halves in
conjunction with the predicted reward magnitudes that would have been associated
with each of these choices. Contrary to the proposal that thirst results in impatience,
and thus more early SIDs during the first half of each session, both monkeys waited
significantly longer in the first half of each session before indicating SIDs
(Supplementary table 2).
monkey
Second half of choices (SID
time)
2711+/- 732 ms (n=3253)
Rank sum test
CS
First half of choices (SID
time)
2874 +/- 661 ms (n=3241)
AR
3074 +/- 668 ms (n=4632)
3026 +/- 658 ms (n=4677)
P<0.001
P<0.001
Supplementary table 2: Average time of SIDs occurrence calculated separately for the first
half of each experimental session and for the second half of the experimental session. Both
monkeys waited significantly longer before they indicated a SID during the first half of the
sessio, than during the second half of the session. This indicates that, although they may
have been thirstier during the first half, this did not result in impatience, whereby they were
more likely to indicate a SID early on in the hope to obtain a reward. To the contrary, it could
indicate that they were more accurate, such that they only indicate early SIDs when they were
sure that a stimulus had been presented, while they waited otherwise for the hazard function
to max out before indicating a SID.
Waiting time during the experimental session and associated reward likelihood.
It might moreover be argued that the differences in SID timing which occurred during
a single session (with monkeys being slightly more patient early in every session),
could account for the difference in reward yield on its own, i.e. the first half of every
session resulting in a different reward yield than the second half, without any direct
4
relation to early vs. late SIDs. Contrary to such a proposal we found no significant
difference between the reward magnitudes that were associated with the two different
groups (first half of SIDs of each daily session vs. second half of SIDs of each daily
session). However, when subdividing each of these two daily halves into early vs. late
SIDs (taking the cut offs described in the main paper), we found that the predicted
reward magnitude was significantly larger with late SIDs for both monkeys for either
half of a session (first half and second half) when compared to either of the early SID
groups (1-way ANOVA, p<0.01, Tukey’s test). At the same time there was no
significant difference regarding the predicted reward magnitude between the two early
SID groups and there was no significant difference when comparing the predicted
reward magnitude between the two late SID groups (p>0.05, post-hoc testing, Tukey’s
test). We take this as evidence that our results reported in the main paper were not due
to the fact that timing of SIDs changed during the course of a daily session. Rather,
increases in predicted reward probability were due to the fact that a choice
corresponded to a late SID and not to an early SID.
Eye movement controls
During the task the animals were required to fixate and keep their eyes within
+/- 1° of the fixation spot. Eye movements occur within even smaller windows, and
previous studies have shown that small eye-movements (within +/- 1.5° windows)
across structured randomly moving backgrounds can elicit neuronal activity in area
MT 2. Thus eye movements across the structured background might have elicited
neuronal activity in MT on some trials, which may have triggered the animal’s
response. To analyse whether eye-movements may have been responsible for our
5
results we took 2 different approaches. Firstly, we excluded trials from our sample
during which eye-movements or eye position deviated by more than +/- 0.15° from
the fixation spot during the last 700 ms to 100 ms prior to SIDs. We thus generated a
sample of trials that was virtually free of eye movements. The procedure slightly
reduced our cell sample, because we still required a minimum of 5 trials for each SID
type for each cell recorded (n=122 cells instead of 138). For this selection of trials
(and cells) we found the same pattern of results as described in the main paper. The
median activity associated with the different SID types following additional eyemovement control is presented in table 1 of the main paper. The activity ratios
between the different SID groups also produced similar results to those reported in the
main paper when trials with eye-movements were eliminated. The mean of the log
ratios of the PDearlySID/APDearlySID distribution was 0.207, it was 0.055 for the
PDlateSID/APDlateSID, 0.104 for the PDearlySID/PDlateSID, and -0.048 for the APDearlySID /
APDlateSID distribution (H0: μPDearlySID = μPDlateSID = μAPDearlySID = μAPDlateSID;
p<0.001, RM ANOVA Tukey’s test). It could be argued that microsaccades or small
pursuit eye movements can still occur within the remaining window of +/- 0.15°, and
the related retinal motion might be sufficient to result in systematic activity changes in
directionally selective MT neurons 2. As an additional control we thus eliminated
trials from the sample where either (A) the eye-velocity exceeded 10°/s during the
period from 700 ms to 100 ms prior to the SID thereby eliminating trials where
microsaccades could confound the result, or (B) the eye position changed by more
than 0.1° during any 50 ms period (equivalent to an eye drift with a velocity of
>=2°/s) from 700 ms to 100 ms prior to the SID. For these eye-movement analyses
we employed the same procedures as described by Bair and O’Keefe 2. The latter
procedures resulted in a further reduction of cells from our sample (due to the
6
required 5 trials per cell per condition; remaining cells n=118), but corroborated all
previous analyses. We found a significantly higher choice probability for early SIDs
than for late SIDs (median [25-75percentile] choice probabilityearly: 0.560 [0.486
0.619]; choice probabilitylate: 0.498 [0.421 0.565]; p<0.001, signed rank test).
Moreover, the activity with early choices in preferred direction was significantly
higher than the activity with early choices in anti-preferred direction, and higher than
with late choices in either preferred direction or anti-preferred direction. Neither of
the latter 3 differed significantly from one another (p<0.001, Kruskal-Wallis RMANOVA, for additional detail see table 1). Thus, eliminating trials where the eyeposition deviated by more than +/- 0.15° from the fixation spot, or eliminating trials
where micro-saccades or drifts/pursuits occurred did not change the basic pattern of
results.
In addition to these controls we calculated whether the direction of micro-saccades
(provided they occurred on a given trial) had an effect on neuronal activity. We first
analysed the number of micro-saccades that occurred within the +/- 0.15 ° window,
which we used for our post-hoc control. For micro-saccade detection we used
published criteria2. Even within the small window of +/- 0.15 ° micro-saccades still
occurred, but they were rare in numbers. In Monkey AR 9156/9850 (92.9%) trials for
which neuronal activity was available were free of micro-saccades (during the time
window from -700 to -100 ms before SID occurrence). In monkey CS this was the
case for 19829/23467 (84.5%) of the trials for which neuronal activity was analysed.
For trials during which micro-saccades occurred we constructed the saccade triggered
averaged responses. This was done separately for saccades that caused retinal slip in
preferred direction and saccades that caused retinal slip in anti-preferred direction.
7
The associated responses were largely flat, and no consistent difference in neuronal
activity with these two types of retinal slip was found (see Supplementary Figure 1).
From these controls we conclude that activity elicited by eye movements was not
responsible for the differential activity reported in the main paper.
Supplementary Figure 1: Relation of neuronal activity to the direction of micro-saccades.
Saccade triggered average responses were constructed from all saccades that were within +/30 ° of the preferred direction of the neuron. If micro-saccades had a substantial X- and Ycomponent, the larger of the two was taken to generate the above eye-movement average.
8
Real world movement coordinates were reflected such that micro-saccades in preferred
direction of a neuron yielded positive amplitudes (black line, dashed lines show s.e.m), while
those in anti-preferred direction yielded negative amplitudes (grey line, grey shaded area
shows s.e.m.). The average amplitude of the micro-saccades that occurred was ~0.15 °. The
upper panel shows the neuronal activity associated with the two different types of microsaccades. The black line shows the activity that occurred with micro-saccades in preferred
direction (i.e. the retinal slip was in anti-preferred direction, based on 1051 trials). Dashed
lines show s.e.m.. The grey line shows the mean activity associated with micro-saccades in
anti-preferred direction, i.e. with retinal slip in preferred direction (based on 1046 trials, grey
shaded area shows s.e.m). There was no consistent increase of activity following retinal slip
in preferred direction, or activity decrease following retinal slip in anti-preferred direction.
Note that the same micro-saccade could contribute to saccades in preferred direction as well
as to saccades in anti-preferred direction, as simultaneously recorded neurons could have
opposite preferred direction. Hence the large similarity between the micro-saccades in
preferred and those in anti-preferred direction.
Fine rate correlation as a function of SID type
We analysed whether fine rate correlation correlated with the choice type and
direction for cell pairs provided at least 10 trials for each SID type occurred during
recording. The median numbers of trials for the different SID types for the pairs from
the 60° group (see main paper, n=46 pairs) were: PDearlySID: n=32 [min: 12, max: 119],
APDearlySID: n=56 [min: 11, max: 128], PDlateSID: n=38 [min: 14, max: 91], APDlateSID:
n=43 [min: 17, max: 139]). For the 120° group (n=68), the median number of trials
for the different SID types was: PDearlySID: n=41 [min: 12, max: 187], APDearlySID:
n=48 [min: 11, max: 153], PDlateSID: n=55 [min: 23, max: 131], APDlateSID: n=55 [min:
14, max: 139]).
Supplementary Figure 2 shows the average neuronal fine rate
correlation as a function of time for the 4 different SID types. Fine rate correlation
9
was measured in a sliding window of 200 ms width, starting at 800 ms (centred)
before SID occurrence. Fine rate correlation strength was determined by calculating
the correlation coefficient3 within a time window of -20 to 20 ms relative to the
trigger spike. Supplementary Figure 2 shows that fine rate correlation increased at
around 500 ms before SIDs in preferred direction, this increase peaked ~300ms before
the SIDs and then returned to baseline. No such increase occurred for early SID in
anti-preferred direction or any of the late SIDs.
Supplementary Figure 2: Time resolved neuronal fine rate correlation (expressed by
the correlation coefficient) for the 4 different SID types. Fine rate correlation was averaged
across neurons that had a preferred direction within 0-120° of another (n=110). Fine rate
correlation was calculated in a sliding window of 200 ms widths. It increased prior to SIDs in
preferred direction shortly before the choice was indicated.
Distributions of fine rate correlation coefficient for the different choice
types
Supplementary Figure 3 shows the distributions of the fine rate correlation
coefficient for the different choice types. For ease of comparison pair-wise analysis of
fine rate correlation strength for different choice types is shown. The figure shows
10
that fine rate correlation was stronger for the 60° group (additional detail is given in
the main paper). Moreover, strength of fine rate correlation was also more consistent
(i.e. better correlated) between different choice types for this group of neurons than
for the 120° group (see insets of correlation coefficients and the associated p-values in
each sub-panel).
On top of the ongoing fine rate correlation (evident by the
significant correlation between fine rate correlation values), choice type modulated
the exact strength of fine rate correlation. The lack of correlation between choice
types for the 120° cell group suggests that these neurons are less strongly coupled,
less consistently coupled, and/or share either less common input. Moreover the
absence of a significant effect of choice type on the strengths of fine rate correlation
(see main paper) suggests that these neuronal pairs are also less modulated by
‘cognitive signals’, or alternatively, if fine rate correlation arises locally, contribute
less to ‘cognitive decisions’.
11
Supplementary Figure 3: Distributions of fine rate correlation strength (correlation coefficient
between simultaneously recorded neurons). Distributions are plotted pair-wise for different
choice types to aid comparison. Upper plots (yellow histograms) show fine rate correlation
12
strength for neurons that shared a similar preferred direction (the 60° group), while lower plots
(red histograms) show fine rate correlation strength between neurons of less similar preferred
direction (120° group). Fine rate correlation was stronger for the 60° group than the 120°
group. Moreover fine rate correlation seemed more consistent for the former group, evident
by significant correlations between fine rate correlation coefficients across different choice
types. Note, that despite overall larger consistency in the 60° group, strength of fine rate
correlation significantly depended on SID type for the 60° group (larger for early SIDs in
preferred direction), while this was not the case for the 120° group.
In addition to similarity in preferred direction cooperation between neurons
might be affected by other covariates. Another obvious candidate is the amount of
receptive field overlap, as overlap determines the amount of common input and the
likelihood of interconnectedness through lateral connections. To determine how
receptive field overlap affects strength of fine rate correlation we analysed whether
fine rate correlation depended on the receptive field overlap for each of the two
groups (the 60° and the 120° group). Overlap of receptive field was expressed as the
total overlap in receptive field area (deg2) divided by the sum of the individual
receptive field areas. Each of the two groups (the 60° and the 120° group) was then
subdivided into two further subgroups that were separated according to the median
receptive field overlap. We refer to these subgroups as the ‘less overlap’ and ‘more
overlap’ groups.
The 60° group with more overlap had a mean overlap of 0.5678+/-0.1736,
while the group with less overlap had a mean overlap of 0.0811+/- 0.1154. The 120°
group with more overlap had a mean overlap in receptive field area of 0.5160+/0.2667, while the group with less overlap had a mean overlap of 0.0142+/- 0.0334.
Overall we did not find a significant difference regarding receptive field overlap
between the 60° and 120° group (p=0.1717, Wilcoxon rank sum test). However, the
13
two less overlap groups were significantly different (p=0.036, Wilcoxon rank sum
test), while the groups with more overlap were not (p=0.2788, Wilcoxon rank sum
test).
We performed a 2-factor ANOVA to determine whether angular difference
(60° vs. 120°), or the amount of receptive field overlap (upper half of overlap for each
of the angular groups vs. lower half of overlap) had a significant effect on the
strengths of neuronal fine rate correlation. We found a significant main effect of
angular difference (p<0.0001, 2 Factor ANOVA), and a significant main effect of RF
group (p=0.0391, 2 Factor ANOVA), but no interaction (p=0.2195, 2 Factor
ANOVA). The average strength of neuronal fine rate correlation for both subgroups
from both groups is shown in Supplementary Figure 4.
Supplementary Figure 4: Strength of fine rate correlation as a function of similarity in
preferred direction and receptive field overlap for the different SID types. Fine rate correlation
was calculated as the neuronal correlation coefficient from spikes occurring from -500 to 100ms before the choice. The correlation coefficient was calculated over a window of -20 ms
to 20 ms relative to the trigger spike. Bars show mean correlation coefficients, error bars
show S.E.M.
14
For both subgroups from both groups we performed a repeated measurement ANOVA
to determine whether SID time (early vs. late), direction (early vs. late), or an
interaction between these two significantly affected the strength of fine rate
correlation. Fine rate correlation was significantly affected by SID type for the 60°
group with more receptive field overlap. Although fine rate correlation did not depend
on SID direction (p=0.1325) or SID time (p=0.5955) alone, it depended significantly
on the interaction between SID time and direction (p=0.0254, RM-ANOVA, n=21)
for this group/subgroup. For the 60° group with less RF overlap neither of the
individual factors determine the strength of fine rate correlation (time: p= 0.4473,
direction: p= 0.7025), nor did the interaction (time*direction: p= 0.5067, RMANOVA, n=21). Neither of the two subgroups from the 120° group was significantly
affected by SID time, SID direction or an interaction between the two (p>0.05, 2
factor RM-ANOVA, n=34 each subgroup). While this could be taken as evidence
that fine rate correlation strength in the 120° group was unrelated to SID type, it can
be seen from figure 8 B (main paper) and Supplementary Figure 4, that early SIDs in
preferred direction were still preceded by the largest amount of fine rate correlation on
average, while early SIDs in anti-preferred direction were preceded by the lowest
average fine rate correlation. Splitting of the groups reduced sample size, and thus the
strength of the effect had to be relatively larger to reach significance. This is probably
why we found a significant effect of SID direction for the 120° group as a whole (see
main paper), but not when splitting it according to receptive field overlap. These
details aside, the analysis determined that the strength of fine rate correlation was
largest prior to early SIDs in preferred direction, provided the neurons shared a
15
similar preferred direction and had more receptive field overlap. One would predict
that precisely these neuron pairs would be best suited to contribute to directional
decision in the absence of external visual motion.
Neuronal fine rate correlation as a function of multi and single unit
recording
We recorded activity simultaneously from single units and from multi units. We
recorded from a total of 30 single-single unit pairs whose preferred direction was less
than 120° apart. The number of multi-multi unit pairs contributing to the paper is 35
and the number of single-multi unit pairs contributing to the data in the paper is 46.
Given the relatively low pair numbers for each of these three subgroups we did not
further separate them according to more similar preferred direction (<60° apart) and
less similar preferred direction (60-120° apart), or according to receptive field size
overlap. We find that the strength of fine rate correlation for all these pairs is largest
for early SIDs in preferred direction, while it is lowest for early SIDs in anti-preferred
direction, although this was only significant for the group of single-multi pairs
(p=0.021, signed rank test). We found that single-single unit pairs exhibited the
overall lowest fine rate correlation (mean correlation coefficient for early SIDs in
preferred direction: 0.0397 +/-0.126), while single-multi unit (mean correlation
coefficient for early SIDs in preferred direction: 0.0918 +/- 0.173) and multi-multi
unit (mean correlation coefficient for early SIDs in preferred direction: 0.115 +/0.180) pairs exhibited fairly similar levels of fine rate correlation. The differences in
fine rate correlation between the three groups were not significant (p>0.05, ANOVA
on ranks), probably due to the relatively small sample size.
16
Coarse rate correlation as a function of SID type
We performed similar analyses as described above for the coarse rate
correlation data.
Supplementary Figure 5 shows the distributions of coarse rate
correlation for the different SID types separately for the two neuron groups (the 60°
and the 120° group). In line with the data concerning neuronal fine rate correlation,
coarse rate correlation was stronger for the 60° group than for the 120° group (for
additional detail see main paper), and more consistent between different choice types
(see insets of correlation coefficients and the associated p-values in each sub-panel).
We performed a 2-factor ANOVA to determine whether angular difference
(60° vs. 120°), or the amount of receptive field overlap (upper half of overlap for each
of the angular groups vs. lower half of overlap) had a significant effect on the coarse
rate correlation. We found a significant main effect of angular difference (p=0.0225, 2
Factor ANOVA), and a significant main effect of RF group (p=0.0455, 2 Factor
ANOVA), and a significant interaction (p=0.0415, 2 Factor ANOVA). The average
strength of coarse rate correlation for both subgroups from both groups is shown in
Supplementary Figure 6.
17
Supplementary Figure 5: Distributions of coarse rate correlation from simultaneously
recorded neurons. Distributions are plotted as pair-wise comparisons for different choice
types to aid comparison. Upper plots (yellow histograms) show coarse rate correlation for
neurons that shared a similar preferred direction (the 60° group), while lower plots (red
18
histograms) show coarse rate correlation between neurons of less similar preferred direction
(120° group). Coarse rate correlation was stronger for the 60° group than the 120° group.
Moreover coarse rate correlation seemed more consistent for the 60° group, evident by
significant correlations between coarse rate correlation values across different choice types.
Despite overall larger consistency, coarse rate correlation significantly depended on SID type
for the 60° group (larger for early SIDs in preferred direction), while this was not the case for
the 120° group.
For both subgroups (more overlap vs. less overlap) from both groups (the 60° and the
120° group) we performed a repeated measurement ANOVA to determine whether
SID time (early vs. late), direction (early vs. late), or an interaction between these two
significantly affected coarse rate correlation. Coarse rate correlation was significantly
affected by SID type for the 60° group with more receptive field overlap. Although it
did not depend on SID direction (p=0.2484) or SID time (p=0.5091) alone, it
depended significantly on the interaction between SID time and direction (p=0.0493,
RM-ANOVA, n=21) for this group/subgroup. For the 60° group with less RF overlap
neither of the individual factors determined the strength of coarse rate correlation
(time: p= 0.7071, direction: p= 0.8037). There was a trend that an interaction between
SID time and direction influenced strength of coarse rate correlation, but it did not
reach significance (time*direction: p= 0.0797, RM-ANOVA, n=21).
For the
subgroup with less receptive field overlap from the 120° group there was a nonsignificant trend that SID direction (p=0.0560, RM-ANOVA, n=34) affected coarse
rate correlation, while neither SID time (p=0.4444, RM-ANOVA, n=34), nor an
interaction between direction and time (p=0.6030, RM-ANOVA, n=34) showed a
similar trend. SID direction significantly affected coarse rate correlation in the 120°
with more receptive field overlap (p=0.0246, RM-ANOVA, n=34), while SID time
19
(p=0.3706, RM-ANOVA, n=34) had no significant effect on coarse rate correlation
for this group, nor was there a significant interaction between SID direction and time
(p=0.7878, RM-ANOVA, n=34). The analysis showed that coarse rate correlation was
generally strongest before early SIDs in preferred direction, provided the neurons had
more receptive field overlap. Similarity in preferred direction also affected strength of
coarse rate correlation, but its relation to SID type was less consistent. This is
particularly evident by the strength of coarse rate correlations as a function of SID
type for neurons that have neither a similar preferred direction nor have substantial
amount of receptive field overlap (see the black bars in Supplementary figure 6 B). In
the context of the current paper such a result makes sense, if neurons do not code for
the same preferred direction and do not represent the same part of the visual field,
their possible combined contribution to directional decisions in the absence of visual
stimuli is not immediately obvious.
Supplementary Figure 6: Strength of coarse coarse rate correlation as a function of similarity
in preferred direction and receptive field overlap for the different SID types. Coarse rate
20
correlations were calculated from spikes occurring within -500 to -100ms before the choice.
Bars show mean coarse rate correlation, error bars show S.E.M.
1.
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