A small-correlation expansion to quantify information in noisy sensory systems

no code implementations • 24 Nov 2022 • Gabriel Mahuas, Olivier Marre, Thierry Mora, Ulisse Ferrari

Neural networks encode information through their collective spiking activity in response to external stimuli.

A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons

1 code implementation • NeurIPS 2020 • Gabriel Mahuas, Giulio Isacchini, Olivier Marre, Ulisse Ferrari, Thierry Mora

Generalized linear models are one of the most efficient paradigms for predicting the correlated stochastic activity of neuronal networks in response to external stimuli, with applications in many brain areas.

Relevant sparse codes with variational information bottleneck

no code implementations • NeurIPS 2016 • Matthew Chalk, Olivier Marre, Gasper Tkacik

In many applications, it is desirable to extract only the relevant aspects of data.

Prediction of spatio-temporal patterns of neural activity from pairwise correlations

1 code implementation • 1 Mar 2009 • Olivier Marre, Sami El Boustani, Yves Fregnac, Alain Destexhe

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity.

Neurons and Cognition