Dataset Information

Optimal population coding by noisy spiking neurons.

ABSTRACT: In retina and in cortical slice the collective response of spiking neural populations is well described by "maximum-entropy" models in which only pairs of neurons interact. We asked, how should such interactions be organized to maximize the amount of information represented in population responses? To this end, we extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model. We found that as we varied the noise level in single neurons and the distribution of network inputs, the optimal pairwise interactions smoothly interpolated to achieve network functions that are usually regarded as discrete--stimulus decorrelation, error correction, and independent encoding. These functions reflected a trade-off between efficient consumption of finite neural bandwidth and the use of redundancy to mitigate noise. Spontaneous activity in the optimal network reflected stimulus-induced activity patterns, and single-neuron response variability overestimated network noise. Our analysis suggests that rather than having a single coding principle hardwired in their architecture, networks in the brain should adapt their function to changing noise and stimulus correlations.

SUBMITTER: Tkacik G

PROVIDER: S-EPMC2922524 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Optimal population coding by noisy spiking neurons.

Tkacik Gasper G Prentice Jason S JS Balasubramanian Vijay V Schneidman Elad E

Proceedings of the National Academy of Sciences of the United States of America 20100726 32

In retina and in cortical slice the collective response of spiking neural populations is well described by "maximum-entropy" models in which only pairs of neurons interact. We asked, how should such interactions be organized to maximize the amount of information represented in population responses? To this end, we extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model. We found that as ...[more]

PMID: 20660781

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Project description:Background: Timing dysfunctions occur in a number of neurological and psychiatric disorders such as Parkinson's disease, obsessive-compulsive disorder, autism and attention-deficit-hyperactivity disorder. Several lines of evidence show that disrupted timing processing is involved in specific fronto-striatal abnormalities. The striatum encodes reinforcement learning and procedural motion, and consequently is required to represent temporal information precisely, which then guides actions in proper sequence. Previous studies highlighted the temporal scaling property of timing-relevant striatal neurons; however, it is still unknown how this is accomplished over short temporal latencies, such as the sub-seconds to seconds range. Methods: We designed a task with a series of timing behaviors that required rats to reproduce a fixed duration with robust action. Using chronic multichannel electrode arrays, we recorded neural activity from dorso-medial striatum in 4 rats performing the task and identified modulation response of each neuron to different events. Cell type classification was performed according to a multi-criteria clustering analysis. Results: Dorso-medial striatal neurons (n = 557) were recorded, of which 113 single units were considered as timing-relevant neurons, especially the fast-spiking subpopulation that had trial-to-trial ramping up or ramping down firing modulation during the time estimation period. Furthermore, these timing-relevant striatal neurons had to calibrate the spread of their firing pattern by rewarded experience to express the timing behavior accurately. Conclusion: Our data suggests that the dynamic activities of timing-relevant units encode information about the current duration and recent outcomes, which is needed to predict and drive the following action. These results reveal the potential mechanism of time calibration in a short temporal resolution, which may help to explain the neural basis of motor coordination affected by certain physiological or pathological conditions.

Dataset Information

Optimal population coding by noisy spiking neurons.

Publications

Optimal population coding by noisy spiking neurons.

Similar Datasets

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