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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.


Related in: MedlinePlus

Comparison of the predictive value of a single extra spike in real versus simulated spike trains for a floccular Purkinje cell and an FTN. Results for Purkinje cells are in black and FTNs are in red. A, D, Predicted versus actual amplitude of the first principal component (PC) of pursuit behavior based on the optimal linear estimator from the real spike trains. B, E, Predicted versus actual amplitude of the first principal component of pursuit behavior based on the optimal linear estimator from the simulated spike trains. In all four graphs, each symbol shows the results from a single behavioral trial. C, F, Each trace shows the weights for an optimized linear decoder ( in Eq. 2) relating the presence of an extra cell spike at time t on the horizontal axis to the amplitude of the first principal component of eye movement variation. Dark and light curves show results for real data versus simulated spike trains.
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Figure 3: Comparison of the predictive value of a single extra spike in real versus simulated spike trains for a floccular Purkinje cell and an FTN. Results for Purkinje cells are in black and FTNs are in red. A, D, Predicted versus actual amplitude of the first principal component (PC) of pursuit behavior based on the optimal linear estimator from the real spike trains. B, E, Predicted versus actual amplitude of the first principal component of pursuit behavior based on the optimal linear estimator from the simulated spike trains. In all four graphs, each symbol shows the results from a single behavioral trial. C, F, Each trace shows the weights for an optimized linear decoder ( in Eq. 2) relating the presence of an extra cell spike at time t on the horizontal axis to the amplitude of the first principal component of eye movement variation. Dark and light curves show results for real data versus simulated spike trains.

Mentions: We next use simulated spike trains to show that the sensory-induced variation in neural responses supports the informative nature of single spikes. Local noise in the timing of individual spikes, on the other hand, tends to defeat the informative nature of single spikes. We find that simulated spike trains (see Materials and Methods, above) have as much predictive value for the behavioral variation as did the real spike trains. For example, the example Purkinje cell and FTN in Figure 3, A and D, showed correlations of 0.74 and 0.88 between the actual magnitude of the first principal component of eye speed and the prediction of the optimal linear estimator (i.e., Eq. 2). Identical analysis of the simulated spike trains for the same two neurons revealed correlations of 0.67 and 0.88 (Fig. 3B,E). Further, the weights in the linear estimators were very similar for the real and simulated spike trains of both neurons (Fig. 3C,F).


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 the predictive value of a single extra spike in real versus simulated spike trains for a floccular Purkinje cell and an FTN. Results for Purkinje cells are in black and FTNs are in red. A, D, Predicted versus actual amplitude of the first principal component (PC) of pursuit behavior based on the optimal linear estimator from the real spike trains. B, E, Predicted versus actual amplitude of the first principal component of pursuit behavior based on the optimal linear estimator from the simulated spike trains. In all four graphs, each symbol shows the results from a single behavioral trial. C, F, Each trace shows the weights for an optimized linear decoder ( in Eq. 2) relating the presence of an extra cell spike at time t on the horizontal axis to the amplitude of the first principal component of eye movement variation. Dark and light curves show results for real data versus simulated spike trains.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Comparison of the predictive value of a single extra spike in real versus simulated spike trains for a floccular Purkinje cell and an FTN. Results for Purkinje cells are in black and FTNs are in red. A, D, Predicted versus actual amplitude of the first principal component (PC) of pursuit behavior based on the optimal linear estimator from the real spike trains. B, E, Predicted versus actual amplitude of the first principal component of pursuit behavior based on the optimal linear estimator from the simulated spike trains. In all four graphs, each symbol shows the results from a single behavioral trial. C, F, Each trace shows the weights for an optimized linear decoder ( in Eq. 2) relating the presence of an extra cell spike at time t on the horizontal axis to the amplitude of the first principal component of eye movement variation. Dark and light curves show results for real data versus simulated spike trains.
Mentions: We next use simulated spike trains to show that the sensory-induced variation in neural responses supports the informative nature of single spikes. Local noise in the timing of individual spikes, on the other hand, tends to defeat the informative nature of single spikes. We find that simulated spike trains (see Materials and Methods, above) have as much predictive value for the behavioral variation as did the real spike trains. For example, the example Purkinje cell and FTN in Figure 3, A and D, showed correlations of 0.74 and 0.88 between the actual magnitude of the first principal component of eye speed and the prediction of the optimal linear estimator (i.e., Eq. 2). Identical analysis of the simulated spike trains for the same two neurons revealed correlations of 0.67 and 0.88 (Fig. 3B,E). Further, the weights in the linear estimators were very similar for the real and simulated spike trains of both neurons (Fig. 3C,F).

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.


Related in: MedlinePlus