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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

Effect of discharge variability and overall signal magnitude on the predictive value of a spike train. A, B, Each graph plots the mean and standard deviation of the correlation between the real and predicted magnitude of the first principal component of eye speed based on simulated spike trains. Data are plotted as a function of the coefficient of variation (A) or the gain (B) of the artificial spike train. Red and blue symbols indicate analysis of simulations based on a representative FTN or abducens neuron. C−F, Scatter plots, where each symbol shows the results of analyzing the data of a single neuron, and the different plots show the correlation between the real and predicted magnitude of the first principal component as a function of the coefficient of variation of the spike trains (D), the amplitude of the neural signal (E), and the ratio of the signal amplitude divided by the coefficient of variation (C, F). Different colors show results for different classes of neurons.
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Figure 5: Effect of discharge variability and overall signal magnitude on the predictive value of a spike train. A, B, Each graph plots the mean and standard deviation of the correlation between the real and predicted magnitude of the first principal component of eye speed based on simulated spike trains. Data are plotted as a function of the coefficient of variation (A) or the gain (B) of the artificial spike train. Red and blue symbols indicate analysis of simulations based on a representative FTN or abducens neuron. C−F, Scatter plots, where each symbol shows the results of analyzing the data of a single neuron, and the different plots show the correlation between the real and predicted magnitude of the first principal component as a function of the coefficient of variation of the spike trains (D), the amplitude of the neural signal (E), and the ratio of the signal amplitude divided by the coefficient of variation (C, F). Different colors show results for different classes of neurons.

Mentions: For each model neuron, we simulated the time-varying probability of spikes using the regression coefficients from the best-fitting kinematic model (Eq. 3), defining that as a gain of one. We then systematically varied both the CV and a gain factor that multiplied the probability of spiking for a given trial. We than generated 10 sets of simulated spikes for each gain and CV, where a set of simulated spikes included all the trials in the dataset. For each set of simulated spikes, we computed the best linear estimator (Eq. 2) of the first principal component of the eye velocity behavior. We averaged across the 10 sets to estimate the mean and standard deviation of the correlation between the actual and predicted magnitude of the first principle component. Finally, we plotted the results as a function of gain and CV (Fig. 5A,B).


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

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

Effect of discharge variability and overall signal magnitude on the predictive value of a spike train. A, B, Each graph plots the mean and standard deviation of the correlation between the real and predicted magnitude of the first principal component of eye speed based on simulated spike trains. Data are plotted as a function of the coefficient of variation (A) or the gain (B) of the artificial spike train. Red and blue symbols indicate analysis of simulations based on a representative FTN or abducens neuron. C−F, Scatter plots, where each symbol shows the results of analyzing the data of a single neuron, and the different plots show the correlation between the real and predicted magnitude of the first principal component as a function of the coefficient of variation of the spike trains (D), the amplitude of the neural signal (E), and the ratio of the signal amplitude divided by the coefficient of variation (C, F). Different colors show results for different classes of neurons.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Effect of discharge variability and overall signal magnitude on the predictive value of a spike train. A, B, Each graph plots the mean and standard deviation of the correlation between the real and predicted magnitude of the first principal component of eye speed based on simulated spike trains. Data are plotted as a function of the coefficient of variation (A) or the gain (B) of the artificial spike train. Red and blue symbols indicate analysis of simulations based on a representative FTN or abducens neuron. C−F, Scatter plots, where each symbol shows the results of analyzing the data of a single neuron, and the different plots show the correlation between the real and predicted magnitude of the first principal component as a function of the coefficient of variation of the spike trains (D), the amplitude of the neural signal (E), and the ratio of the signal amplitude divided by the coefficient of variation (C, F). Different colors show results for different classes of neurons.
Mentions: For each model neuron, we simulated the time-varying probability of spikes using the regression coefficients from the best-fitting kinematic model (Eq. 3), defining that as a gain of one. We then systematically varied both the CV and a gain factor that multiplied the probability of spiking for a given trial. We than generated 10 sets of simulated spikes for each gain and CV, where a set of simulated spikes included all the trials in the dataset. For each set of simulated spikes, we computed the best linear estimator (Eq. 2) of the first principal component of the eye velocity behavior. We averaged across the 10 sets to estimate the mean and standard deviation of the correlation between the actual and predicted magnitude of the first principle component. Finally, we plotted the results as a function of gain and CV (Fig. 5A,B).

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