21 research outputs found
Stimulus-dependent maximum entropy models of neural population codes
Neural populations encode information about their stimulus in a collective
fashion, by joint activity patterns of spiking and silence. A full account of
this mapping from stimulus to neural activity is given by the conditional
probability distribution over neural codewords given the sensory input. To be
able to infer a model for this distribution from large-scale neural recordings,
we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal
extension of the canonical linear-nonlinear model of a single neuron, to a
pairwise-coupled neural population. The model is able to capture the
single-cell response properties as well as the correlations in neural spiking
due to shared stimulus and due to effective neuron-to-neuron connections. Here
we show that in a population of 100 retinal ganglion cells in the salamander
retina responding to temporal white-noise stimuli, dependencies between cells
play an important encoding role. As a result, the SDME model gives a more
accurate account of single cell responses and in particular outperforms
uncoupled models in reproducing the distributions of codewords emitted in
response to a stimulus. We show how the SDME model, in conjunction with static
maximum entropy models of population vocabulary, can be used to estimate
information-theoretic quantities like surprise and information transmission in
a neural population.Comment: 11 pages, 7 figure
Gibbs distribution analysis of temporal correlations structure in retina ganglion cells
We present a method to estimate Gibbs distributions with
\textit{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 (\cite{marre-boustani-etal:09}) to describe the
statistics of spatio-temporal spike patterns and emphasizes the role of higher
order spatio-temporal interactions.Comment: To appear in J. Physiol. Pari