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Literature DB >> 22115900

Gibbs distribution analysis of temporal correlations structure in retina ganglion cells.

J C Vasquez¹, O Marre, A G Palacios, M J Berry, B Cessac.

Abstract

We present a method to estimate Gibbs distributions with spatio-temporal constraints on spike trains statistics. We apply this method to spike trains recorded from ganglion cells of the salamander retina, in response to natural movies. Our analysis, restricted to a few neurons, performs more accurately than pairwise synchronization models (Ising) or the 1-time step Markov models (Marre et al., 2009) to describe the statistics of spatio-temporal spike patterns and emphasizes the role of higher order spatio-temporal interactions.

Entities: Chemical Disease Gene Species

Mesh：

Year: 2011 PMID： 22115900 PMCID： PMC3424736 DOI： 10.1016/j.jphysparis.2011.11.001

Source DB: PubMed Journal: J Physiol Paris ISSN： 0928-4257

22 in total

Gibbs distribution analysis of temporal correlations structure in retina ganglion cells.

Review 1. Multiple neural spike train data analysis: state-of-the-art and future challenges.

2. Recording spikes from a large fraction of the ganglion cells in a retinal patch.

3. Weak pairwise correlations imply strongly correlated network states in a neural population.

4. The structure of multi-neuron firing patterns in primate retina.

5. Prediction of spatiotemporal patterns of neural activity from pairwise correlations.

6. The architecture of functional interaction networks in the retina.

7. Sparse low-order interaction network underlies a highly correlated and learnable neural population code.

8. Repeated patterns of distributed synchrony in neuronal assemblies.

9. Spatiotemporal activity patterns of rat cortical neurons predict responses in a conditioned task.

10. Statistics of spike trains in conductance-based neural networks: Rigorous results.

1. A generative spike train model with time-structured higher order correlations.

2. Blindfold learning of an accurate neural metric.

3. A Tractable Method for Describing Complex Couplings between Neurons and Population Rate.

4. Searching for collective behavior in a large network of sensory neurons.

5. PRANAS: A New Platform for Retinal Analysis and Simulation.

6. Screening by changes in stereotypical behavior during cell motility.

7. A Moment-Based Maximum Entropy Model for Fitting Higher-Order Interactions in Neural Data.

8. Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains.

9. Information Entropy Production of Maximum Entropy Markov Chains from Spike Trains.

10. Stimulus-dependent maximum entropy models of neural population codes.