Dynamical Neural Networks: modeling low-level vision at short latencies

Laurent Perrinet (1)

1 INCM - Institut de neurosciences cognitives de la méditerranée - UMR 6193

Our goal is to understand the dynamics of neural computations in low-level vision. We study how the substrate of this system, that is local biochemical neural processes, could combine to give rise to an efficient and global perception. We will study these neural computations at different scales from the single-cell to the whole visual system to infer generic aspects of the underlying neural code which may help to understand this cognitive ability. In fact, the architecture of cortical areas, such as the Primary Visual Cortex (V1), is massively parallel and we will focus on cortical columns as generic adaptive micro-circuits. To stress on the dynamical aspect of the processing, we will also focus on the transient response, that is during the first milliseconds after the presentation of a stimulus. In a generic model of a visual area, we propose to study the neural code as implementing visual pattern matching, that is as efficiently inverting a known model of image synthesis...

Mots clés: Neuronal representation; over-complete dictionaries; inverse linear model; distributed probabilistic representation; spike-event computation; efficient coding; Matching Pursuit; Sparse Spike Coding; Sparse Spike Learning

Domaines: Neurosciences [q-bio.NC]

Laurent Perrinet. Dynamical Neural Networks: modeling low-level vision at short latencies. EPJ Special Topics "Topics in Dynamical Neural Networks: From Large Scale Neural Networks to Motor Control and Vision", 2006, 142 (1), pp.163--225. ⟨10.1140/epjst/e2007-00061-7⟩. ⟨hal-00116207v3⟩