International audienceWe propose a numerical method to learn maximum entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers, [10] and [4], which proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows one to properly handle memory effects in spike statistics, for large-sized neural networks
Recent experimental advances have made it possible to record up to several hundreds of neurons simul...
"We consider the maximum entropy Markov chain inference approach to characterize the collective stat...
Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in ne...
We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal c...
We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal c...
International audienceWe propose a numerical method to learn maximum entropy (MaxEnt) distributions ...
We propose a numerical method to learn maximum entropy (MaxEnt) distributions with spatio-temporal c...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
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...
International audienceWe consider a spike-generating stationary Markov process whose transition prob...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
Recent experimental advances have made it possible to record up to several hundreds of neurons simul...
Recent experimental advances have made it possible to record up to several hundreds of neurons simul...
Recent experimental advances have made it possible to record up to several hundreds of neurons simul...
"We consider the maximum entropy Markov chain inference approach to characterize the collective stat...
Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in ne...
We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal c...
We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal c...
International audienceWe propose a numerical method to learn maximum entropy (MaxEnt) distributions ...
We propose a numerical method to learn maximum entropy (MaxEnt) distributions with spatio-temporal c...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
National audienceRecent experimental advances have made it possible to record several hundred neuron...
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...
International audienceWe consider a spike-generating stationary Markov process whose transition prob...
We consider the maximum entropy Markov chain inference approach to characterize the collective stati...
Recent experimental advances have made it possible to record up to several hundreds of neurons simul...
Recent experimental advances have made it possible to record up to several hundreds of neurons simul...
Recent experimental advances have made it possible to record up to several hundreds of neurons simul...
"We consider the maximum entropy Markov chain inference approach to characterize the collective stat...
Understanding how stimuli and synaptic connectivity influence the statistics of spike patterns in ne...