Processing various motion features and measuring RGCs pairwise correlations with a 2D retinal model

International audienc

Biophysical cortical column model for optical signal analysis

International audienceWe propose a biological cortical column model, at a some mesoscopic scale, in order to better understand and start to interpret biological sources of voltagesensitive dye imaging signal. The mesoscopic scale, corresponding to a micro-column, is about 50 ,m. Simulations are done thanks to the NEURON and NEURONCONSTRUCT software. This model suggests that the OI signal is the result of an average from multiple components whose proportion changes with levels of activity and shows surprisingly that inhibitory cells, spiking activity and deep layers may well participate more to the signal than initially though

Processing various motion features and measuring RGCs pairwise correlations with a 2D retinal model

International audienc

Processing various motion features and measuring RGCs pairwise correlations with a 2D retinal model

International audienc

Simulating the cortical activity evoked by artificial retinal implants

International audienc

Sparse deep predictive coding captures contour integration capabilities of the early visual system

International audienceBoth neurophysiological and psychophysical experiments have pointed out the crucial role of recurrent and feedback connections to process context-dependent information in the early visual cortex. While numerous models have accounted for feedback effects at either neural or representational level, none of them were able to bind those two levels of analysis. Is it possible to describe feedback effects at both levels using the same model? We answer this question by combining Predictive Coding (PC) and Sparse Coding (SC) into a hierarchical and convolutional framework applied to realistic problems. In the Sparse Deep Predictive Coding (SDPC) model, the SC component models the internal recurrent processing within each layer, and the PC component describes the interactions between layers using feedforward and feedback connections. Here, we train a 2-layered SDPC on two different databases of images, and we interpret it as a model of the early visual system (V1 & V2). We first demonstrate that once the training has converged, SDPC exhibits oriented and localized receptive fields in V1 and more complex features in V2. Second, we analyze the effects of feedback on the neural organization beyond the classical receptive field of V1 neurons using interaction maps. These maps are similar to association fields and reflect the Gestalt principle of good continuation. We demonstrate that feedback signals reorganize interaction maps and modulate neural activity to promote contour integration. Third, we demonstrate at the representational level that the SDPC feedback connections are able to overcome noise in input images. Therefore, the SDPC captures the association field principle at the neural level which results in a better reconstruction of blurred images at the representational level