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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.


Prediction of eye speed by spike counts in windows of different duration. Each graph plots the correlation between the output of different estimators and the amplitude of the first principal component of eye speed. Pink swatches are the lower bounds for the best linear estimator (Eq. 2). Black lines are from the spike count in windows of different duration centered 125 ms after the onset of target motion. The red line labeled “1000 trials” is the estimate of the upper bound of the results from the best linear estimator. Different graphs show data for different neuron types.
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Figure 7: Prediction of eye speed by spike counts in windows of different duration. Each graph plots the correlation between the output of different estimators and the amplitude of the first principal component of eye speed. Pink swatches are the lower bounds for the best linear estimator (Eq. 2). Black lines are from the spike count in windows of different duration centered 125 ms after the onset of target motion. The red line labeled “1000 trials” is the estimate of the upper bound of the results from the best linear estimator. Different graphs show data for different neuron types.

Mentions: Spike counts in brief windows centered 125 ms after the onset of target motion predicted the magnitude of the first principal component almost as well as did the linear weighting function. For each neuron type, the correlation between the predicted and actual magnitude of the first principal component increased as a function of the duration of the counting interval, and reached an asymptote for intervals of duration 40 to 100 ms (Fig. 7). For Purkinje cells, windows as short as 40 ms provided nearly asymptotic correlations, meaning that a good account of the variation in motor behavior can emerge from just a few spikes (0.04 s × 100 spikes/s = 4 spikes). The asymptotic correlation in Purkinje cells matched the correlation provided by the linear estimator for the actual data, while for the other neurons the asymptotic correlation was somewhat smaller than that provided by the linear estimator. Interestingly, counting spikes is the least informative for abducens neurons, probably because of edge effects created by their very regular spiking pattern.


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

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

Prediction of eye speed by spike counts in windows of different duration. Each graph plots the correlation between the output of different estimators and the amplitude of the first principal component of eye speed. Pink swatches are the lower bounds for the best linear estimator (Eq. 2). Black lines are from the spike count in windows of different duration centered 125 ms after the onset of target motion. The red line labeled “1000 trials” is the estimate of the upper bound of the results from the best linear estimator. Different graphs show data for different neuron types.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Prediction of eye speed by spike counts in windows of different duration. Each graph plots the correlation between the output of different estimators and the amplitude of the first principal component of eye speed. Pink swatches are the lower bounds for the best linear estimator (Eq. 2). Black lines are from the spike count in windows of different duration centered 125 ms after the onset of target motion. The red line labeled “1000 trials” is the estimate of the upper bound of the results from the best linear estimator. Different graphs show data for different neuron types.
Mentions: Spike counts in brief windows centered 125 ms after the onset of target motion predicted the magnitude of the first principal component almost as well as did the linear weighting function. For each neuron type, the correlation between the predicted and actual magnitude of the first principal component increased as a function of the duration of the counting interval, and reached an asymptote for intervals of duration 40 to 100 ms (Fig. 7). For Purkinje cells, windows as short as 40 ms provided nearly asymptotic correlations, meaning that a good account of the variation in motor behavior can emerge from just a few spikes (0.04 s × 100 spikes/s = 4 spikes). The asymptotic correlation in Purkinje cells matched the correlation provided by the linear estimator for the actual data, while for the other neurons the asymptotic correlation was somewhat smaller than that provided by the linear estimator. Interestingly, counting spikes is the least informative for abducens neurons, probably because of edge effects created by their very regular spiking pattern.

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.