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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.


Trade-offs between the duration of the spike counting bin and the size of the population of model neurons. Different color plots show results based on different populations of neurons. Each pixel uses color to indicate the correlation between actual and predicted magnitude of the first principle component of eye speed, for the population size and bin width on the y- and x-axes. The black lines show iso-probability contours.
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Figure 10: Trade-offs between the duration of the spike counting bin and the size of the population of model neurons. Different color plots show results based on different populations of neurons. Each pixel uses color to indicate the correlation between actual and predicted magnitude of the first principle component of eye speed, for the population size and bin width on the y- and x-axes. The black lines show iso-probability contours.

Mentions: To be more systematic, we next evaluated the correlations between the actual and predicted magnitudes of the first component of eye speed for spike counts in bins of width ranging from 1 to 96 ms and for populations of 96 to 1 model neurons. The colored images in Figure 10 show the correlations between actual and predicted magnitude of the first principal component of eye speed for each combination. The graphs include superimposed iso-correlation contours to show the tradeoff between population size and duration of the analysis window. The contours have slopes that range from −2 to −4 in their steep negative regions, indicating that decreases in the width of the analysis window had to be compensated by at least a doubling of the population size to maintain the predictive value of spike counts.


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

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

Trade-offs between the duration of the spike counting bin and the size of the population of model neurons. Different color plots show results based on different populations of neurons. Each pixel uses color to indicate the correlation between actual and predicted magnitude of the first principle component of eye speed, for the population size and bin width on the y- and x-axes. The black lines show iso-probability contours.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 10: Trade-offs between the duration of the spike counting bin and the size of the population of model neurons. Different color plots show results based on different populations of neurons. Each pixel uses color to indicate the correlation between actual and predicted magnitude of the first principle component of eye speed, for the population size and bin width on the y- and x-axes. The black lines show iso-probability contours.
Mentions: To be more systematic, we next evaluated the correlations between the actual and predicted magnitudes of the first component of eye speed for spike counts in bins of width ranging from 1 to 96 ms and for populations of 96 to 1 model neurons. The colored images in Figure 10 show the correlations between actual and predicted magnitude of the first principal component of eye speed for each combination. The graphs include superimposed iso-correlation contours to show the tradeoff between population size and duration of the analysis window. The contours have slopes that range from −2 to −4 in their steep negative regions, indicating that decreases in the width of the analysis window had to be compensated by at least a doubling of the population size to maintain the predictive value of spike counts.

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.