We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties o...
Maximum entropy models have become popular statistical models in neuroscience and other areas of bio...
41 pages, 10 figuresInternational audienceUnderstanding the dynamics of neural networks is a major c...
41 pages, 10 figuresInternational audienceUnderstanding the dynamics of neural networks is a major c...
"We consider the maximum entropy Markov chain inference approach to characterize the collective stat...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
"The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the no...
International audienceWe consider a spike-generating stationary Markov process whose transition prob...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in ne...
We propose a generalization of the existing maximum entropy models used for spike trains statistics ...
We propose a generalization of the existing maximum entropy models used for spike trains statistics ...
We propose a generalization of the existing maximum entropy models used for spike trains statistics ...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
Maximum entropy models have become popular statistical models in neuroscience and other areas of bio...
41 pages, 10 figuresInternational audienceUnderstanding the dynamics of neural networks is a major c...
41 pages, 10 figuresInternational audienceUnderstanding the dynamics of neural networks is a major c...
"We consider the maximum entropy Markov chain inference approach to characterize the collective stat...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
"The spiking activity of neuronal networks follows laws that are not time-reversal symmetric; the no...
International audienceWe consider a spike-generating stationary Markov process whose transition prob...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in ne...
We propose a generalization of the existing maximum entropy models used for spike trains statistics ...
We propose a generalization of the existing maximum entropy models used for spike trains statistics ...
We propose a generalization of the existing maximum entropy models used for spike trains statistics ...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
Maximum entropy models have become popular statistical models in neuroscience and other areas of bio...
41 pages, 10 figuresInternational audienceUnderstanding the dynamics of neural networks is a major c...
41 pages, 10 figuresInternational audienceUnderstanding the dynamics of neural networks is a major c...