Synaptic currents

In the brain, while early sensory areas encode and process external inputs rapidly, higher-association areas are endowed with slow dynamics to benefit information accumulation over time. This property raises the question of why diverse timescales are well localized rather than being mixed up across the cortex, despite high connection density and an abundance of feedback loops that support reliable signal propagation. In this talk, we will address this question by analyzing a large-scale network model of the primate cortex, and we identify a novel dynamical regime termed "interference-free prop

Contrastive Hebbian Learning (CHL) has recently emerged as a new biologically plausible paradigm to explain the mechanisms of plasticity in the brain. In classical CHL, developed for artificial networks, neurons learn in a two-step procedure where their free response to a given input is contrasted to their clamped response given the same input and an imposed target output. This two-step procedure, which is unlikely to be implementable in biological systems, has recently been made more plausible by exploiting the fundamental idea that neurons can learn by predicting their own future activity [1

The connectivity between neurons plays a major role in the dynamics and function of all neural systems. Here, we investigate the joint effects of two previously reported connectivity motifs, partial symmetry and self-coupling, on the dynamics of recurrent neural networks (RNNs). Both motifs can be thought of as statistical deviations from i.i.d. connectivity. In particular, we define self-coupling as the values of diagonal entries and partial symmetry as the correlations across the diagonals of coupling matrices. We calculate the full Lyapunov spectrum to characterize the stability of perturba