277 citations · 1k across the 20 of their papers we have counts for
25 papers
A small-correlation expansion to quantify information in noisy sensory systems
Gabriel Mahuas, Olivier Marre, Thierry Mora +1
Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with comple…
A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons
Gabriel Mahuas, Giulio Isacchini, Olivier Marre +2
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 appli…
Predicting synchronous firing of large neural populations from sequential recordings
Oleksandr Sorochynskyi, Stéphane Deny, Olivier Marre +1
A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing…
Optogenetic vision restoration with high resolution
Ulisse Ferrari, Stéphane Deny, Abhishek Sengupta +6
The majority of inherited retinal degenerations are due to photoreceptor cell death. In many cases ganglion cells are spared making it possible to stimulate them to restore visual…
Modeling the correlated activity of neural populations: A review
Christophe Gardella, Olivier Marre, Thierry Mora
The principles of neural encoding and computations are inherently collective and usually involve large populations of interacting neurons with highly correlated activities. While t…
Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
Ulisse Ferrari, Stephane Deny, Matthew Chalk +3
Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend stron…