2022-11-24 : CONECT seminar by Bruno Cessac

“Retinal processing: Insights from mathematical modelling”.

2022-11-24 2:00 PM

During this CONECT seminar, Bruno Cessac did present his recent work on “Retinal processing: Insights from mathematical modelling”:

The retina is the entrance of the visual system. Although based on common biophysical principles, the dynamics of retinal neurons are quite different from their cortical counterparts, raising interesting problems for modellers. In this paper, I address some mathematically stated questions in this spirit, discussing, in particular: (1) How could lateral amacrine cell connectivity shape the spatio-temporal spike response of retinal ganglion cells? (2) How could spatio-temporal stimuli correlations and retinal network dynamics shape the spike train correlations at the output of the retina? These questions are addressed, first, introducing a mathematically tractable model of the layered retina, integrating amacrine cells’ lateral connectivity and piecewise linear rectification, allowing for computing the retinal ganglion cells receptive field together with the voltage and spike correlations of retinal ganglion cells resulting from the amacrine cells networks. Then, I review some recent results showing how the concept of spatio-temporal Gibbs distributions and linear response theory can be used to characterize the collective spike response to a spatio-temporal stimulus of a set of retinal ganglion cells, coupled via effective interactions corresponding to the amacrine cells network. On these bases, I briefly discuss several potential consequences of these results at the cortical level. Keywords: retinal network; visual system; spatio-temporal spike correlations; linear response; non stationarity

My research was initially modeling and analysis of large sized dynamical systems arising in various fields such as physics, biology, sociology, computers networks. I have worked on subjects such as self-organized criticality, linear response in chaotic systems, social networks, communications networks. My main interest concerns neuronal networks dynamics. I have developed methods combining dynamical systems theory, statistical physics and ergodic theory allowing to classify dynamics arising in canonical neuronal networks models like integrate and fire models or firing rate models. I have applied these methods for the study of synaptic and intrinsic plasticity, dynamical learning, spike coding, spike train statistics analysis, mean-field dynamics. I am now involved in developing models for the visual system, especially the retina, as well as numerical methods and software for neuroscientists.
Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.