277 citations · 1k across the 20 of their papers we have counts for
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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…
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…
A simple model for low variability in neural spike trains
Ulisse Ferrari, Stephane Deny, Olivier Marre +1
Neural noise sets a limit to information transmission in sensory systems. In several areas, the spiking response (to a repeated stimulus) has shown a higher degree of regularity th…
Maximum entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity
Trang-Anh Nghiem, Bartosz Telenczuk, Olivier Marre +2
Maximum Entropy models can be inferred from large data-sets to uncover how collective dynamics emerge from local interactions. Here, such models are employed to investigate neurons…
A biophysical model explains the spontaneous bursting behavior in the developing retina
Dora Matzakou-Karvouniari, Lionel Gil, Elaine Orendorff +3
During early development, waves of activity propagate across the retina and play a key role in the proper wiring of the early visual system. During the stage II these waves are tri…
Pairwise Ising model analysis of human cortical neuron recordings
Trang-Anh Nghiem, Olivier Marre, Alain Destexhe +1
During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investi…
Blindfold learning of an accurate neural metric
Christophe Gardella, Olivier Marre, Thierry Mora
The brain has no direct access to physical stimuli, but only to the spiking activity evoked in sensory organs. It is unclear how the brain can structure its representation of the w…
Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding
Ulisse Ferrari, Christophe Gardella, Olivier Marre +1
According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for s…
A tractable method for describing complex couplings between neurons and population rate
Christophe Gardella, Olivier Marre, Thierry Mora
Neurons within a population are strongly correlated, but how to simply capture these correlations is still a matter of debate. Recent studies have shown that the activity of each c…
Nonlinear decoding of a complex movie from the mammalian retina
Vicente Botella-Soler, Stéphane Deny, Olivier Marre +1
Retinal circuitry transforms spatiotemporal patterns of light into spiking activity of ganglion cells, which provide the sole visual input to the brain. Recent advances have led to…
Dynamical criticality in the collective activity of a population of retinal neurons
Thierry Mora, Stéphane Deny, Olivier Marre
Recent experimental results based on multi-electrode and imaging techniques have reinvigorated the idea that large neural networks operate near a critical point, between order and…
High accuracy decoding of dynamical motion from a large retinal population
Olivier Marre, Vicente Botella-Soler, Kristina D. Simmons +3
Motion tracking is a challenge the visual system has to solve by reading out the retinal population. Here we recorded a large population of ganglion cells in a dense patch of salam…
Thermodynamics for a network of neurons: Signatures of criticality
Gasper Tkacik, Thierry Mora, Olivier Marre +3
The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increa…
Predictive information in a sensory population
Stephanie E. Palmer, Olivier Marre, Michael J. Berry +1
Guiding behavior requires the brain to make predictions about future sensory inputs. Here we show that efficient predictive computation starts at the earliest stages of the visual…
Searching for collective behavior in a network of real neurons
Gašper Tkačik, Olivier Marre, Dario Amodei +3
Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this pri…
Spatio-temporal spike trains analysis for large scale networks using maximum entropy principle and Monte-Carlo method
Hassan Nasser, Olivier Marre, Bruno Cessac
Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main sta…
Learning quadratic receptive fields from neural responses to natural stimuli
Kanaka Rajan, Olivier Marre, Gašper Tkačik
Models of neural responses to stimuli with complex spatiotemporal correlation structure often assume that neurons are only selective for a small number of linear projections of a p…
The simplest maximum entropy model for collective behavior in a neural network
Gasper Tkacik, Olivier Marre, Thierry Mora +3
Recent work emphasizes that the maximum entropy principle provides a bridge between statistical mechanics models for collective behavior in neural networks and experiments on netwo…
Gibbs distribution analysis of temporal correlations structure in retina ganglion cells
J. C. Vasquez, O. Marre, A. G. Palacios +2
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 ganglio…
Prediction of spatio-temporal patterns of neural activity from pairwise correlations
Olivier Marre, Sami El Boustani, Yves Fregnac +1
We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption,…