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Structure and sensitivity of neural population responses in the retina

Abstract : Ganglion cells form the output of the retina: they transfer visual information from the eye to the brain. How they represent information is still debated. Their responses to visual stimuli are highly nonlinear, exhibit strong correlations between neurons, and some information is only present at the population level. I first study the structure of population responses. Recent studies have shown that cortical cells are influenced by the summed activity of neighboring neurons. However, a model for these interactions was still lacking. I describe a model of population activity that reproduces the coupling between each cell and the population activity. Neurons in the salamander retina are found to depend in unexpected ways on the population activity. I then describe a method to characterize the sensitivity of rat retinal neurons to perturbations of a stimulus. Closed-loop experiments are used to explore selectively the space of perturbations around a given stimulus. I show that responses to small perturbations can be described by a local linearization of their probability, and that their sensitivity exhibits signatures of efficient coding. Finally, I show how the sensitivity of neural populations can be estimated from response structure. I show that Restricted Boltzmann Machines (RBMs) are accurate models of neural correlations. To measure the discrimination power of neural populations, I search for a neural metric such that responses to different stimuli are far apart and responses to the same stimulus are close. I show that RBMs provide such neural metrics, and outperform classical metrics at discriminating small stimulus perturbations.
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Submitted on : Thursday, November 22, 2018 - 2:53:06 PM
Last modification on : Sunday, October 25, 2020 - 4:59:25 PM
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  • HAL Id : tel-01931168, version 1


Christophe Gardella. Structure and sensitivity of neural population responses in the retina. Biological Physics []. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066603⟩. ⟨tel-01931168⟩



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