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The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem.

Chaisanguanthum KS, Joshua M, Medina JF, Bialek W, Lisberger SG - eNeuro (2014)

Bottom Line: Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs.We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population.As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sloan-Swartz Center for Theoretical Neurobiology and Center for Integrative Neuroscience, Department of Physiology, University of California , San Francisco, San Francisco, California 94143.

ABSTRACT
A single extra spike makes a difference. Here, the size of the eye velocity in the initiation of smooth eye movements in the right panel depends on whether a cerebellar Purkinje cell discharges 3 (red), 4 (green), 5 (blue), or 6 (black) spikes in the 40-ms window indicated by the gray shading in the rasters on the left. Spike trains are rich in information that can be extracted to guide behaviors at millisecond time resolution or across longer time intervals. In sensory systems, the information usually is defined with respect to the stimulus. Especially in motor systems, however, it is equally critical to understand how spike trains predict behavior. Thus, our goal was to compare systematically spike trains in the oculomotor system with eye movement behavior on single movements. We analyzed the discharge of Purkinje cells in the floccular complex of the cerebellum, floccular target neurons in the brainstem, other vestibular neurons, and abducens neurons. We find that an extra spike in a brief analysis window predicts a substantial fraction of the trial-by-trial variation in the initiation of smooth pursuit eye movements. For Purkinje cells, a single extra spike in a 40 ms analysis window predicts, on average, 0.5 SDs of the variation in behavior. An optimal linear estimator predicts behavioral variation slightly better than do spike counts in brief windows. Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs. We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population. As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation.

No MeSH data available.


Comparison of predictions made by spike counts over 40 ms in time versus space. Each panel shows data for a different group of neurons. Within each panel, the traces show the correlation between the actual and predicted amplitude of the first principal component of eye speed as a function of the time at the center of the analysis window. Blue traces show the results for counting spikes in a 40 ms window; the traces end at 180 ms because of the width of the analysis window. Green traces show the result for counting spikes in a 1 ms window across the simulated spike trains of 40 different neurons.
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Figure 9: Comparison of predictions made by spike counts over 40 ms in time versus space. Each panel shows data for a different group of neurons. Within each panel, the traces show the correlation between the actual and predicted amplitude of the first principal component of eye speed as a function of the time at the center of the analysis window. Blue traces show the results for counting spikes in a 40 ms window; the traces end at 180 ms because of the width of the analysis window. Green traces show the result for counting spikes in a 1 ms window across the simulated spike trains of 40 different neurons.

Mentions: We compared the predictions from counting spikes across a population of neurons versus counting spikes across time in a single neuron. In each case, we asked how well the spike count predicted the magnitude of the first principal component of eye speed. As before, we created a population of N neurons with M trials of target motion. For the M trials, we used the different trajectories of the initiation of pursuit taken from the data and the approach outlined in Materials and Methods, above, to generate N × M spike trains. An example of the results appears in Figure 9, which plots the magnitude of the correlation as a function of the time of the center of the counting interval. We obtained better correlations between the first principal component of eye speed and spikes counts in a 40 ms window in individual neurons (Fig. 9, blue traces), versus counts from 40 different neurons in a 1 ms window (Fig. 9, green traces). The difference was present in all four groups of neurons we studied.


The Neural Code for Motor Control in the Cerebellum and Oculomotor Brainstem.

Chaisanguanthum KS, Joshua M, Medina JF, Bialek W, Lisberger SG - eNeuro (2014)

Comparison of predictions made by spike counts over 40 ms in time versus space. Each panel shows data for a different group of neurons. Within each panel, the traces show the correlation between the actual and predicted amplitude of the first principal component of eye speed as a function of the time at the center of the analysis window. Blue traces show the results for counting spikes in a 40 ms window; the traces end at 180 ms because of the width of the analysis window. Green traces show the result for counting spikes in a 1 ms window across the simulated spike trains of 40 different neurons.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596133&req=5

Figure 9: Comparison of predictions made by spike counts over 40 ms in time versus space. Each panel shows data for a different group of neurons. Within each panel, the traces show the correlation between the actual and predicted amplitude of the first principal component of eye speed as a function of the time at the center of the analysis window. Blue traces show the results for counting spikes in a 40 ms window; the traces end at 180 ms because of the width of the analysis window. Green traces show the result for counting spikes in a 1 ms window across the simulated spike trains of 40 different neurons.
Mentions: We compared the predictions from counting spikes across a population of neurons versus counting spikes across time in a single neuron. In each case, we asked how well the spike count predicted the magnitude of the first principal component of eye speed. As before, we created a population of N neurons with M trials of target motion. For the M trials, we used the different trajectories of the initiation of pursuit taken from the data and the approach outlined in Materials and Methods, above, to generate N × M spike trains. An example of the results appears in Figure 9, which plots the magnitude of the correlation as a function of the time of the center of the counting interval. We obtained better correlations between the first principal component of eye speed and spikes counts in a 40 ms window in individual neurons (Fig. 9, blue traces), versus counts from 40 different neurons in a 1 ms window (Fig. 9, green traces). The difference was present in all four groups of neurons we studied.

Bottom Line: Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs.We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population.As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation.

View Article: PubMed Central - HTML - PubMed

Affiliation: Sloan-Swartz Center for Theoretical Neurobiology and Center for Integrative Neuroscience, Department of Physiology, University of California , San Francisco, San Francisco, California 94143.

ABSTRACT
A single extra spike makes a difference. Here, the size of the eye velocity in the initiation of smooth eye movements in the right panel depends on whether a cerebellar Purkinje cell discharges 3 (red), 4 (green), 5 (blue), or 6 (black) spikes in the 40-ms window indicated by the gray shading in the rasters on the left. Spike trains are rich in information that can be extracted to guide behaviors at millisecond time resolution or across longer time intervals. In sensory systems, the information usually is defined with respect to the stimulus. Especially in motor systems, however, it is equally critical to understand how spike trains predict behavior. Thus, our goal was to compare systematically spike trains in the oculomotor system with eye movement behavior on single movements. We analyzed the discharge of Purkinje cells in the floccular complex of the cerebellum, floccular target neurons in the brainstem, other vestibular neurons, and abducens neurons. We find that an extra spike in a brief analysis window predicts a substantial fraction of the trial-by-trial variation in the initiation of smooth pursuit eye movements. For Purkinje cells, a single extra spike in a 40 ms analysis window predicts, on average, 0.5 SDs of the variation in behavior. An optimal linear estimator predicts behavioral variation slightly better than do spike counts in brief windows. Simulations reveal that the ability of single spikes to predict a fraction of behavior also emerges from model spike trains that have the same statistics as the real spike trains, as long as they are driven by shared sensory inputs. We think that the shared sensory estimates in their inputs create correlations in neural spiking across time and across each population. As a result, one or a small number of spikes in a brief time interval can predict a substantial fraction of behavioral variation.

No MeSH data available.