Topic: covariance plasticity

Recurrent network models of excitatory (E) and inhibitory (I) neurons with supralinear activation functions have successfully explained several cortical computations, including response normalization and surround suppression. Unlike more abstract approaches, such networks allow direct comparisons with experimentally measured neural activities and synaptic strengths. However, the scope of these networks remained limited as their connectivity needed to be designed by hand or fitted by complex machine learning algorithms to yield stable activity and computations. Here we present a method to effic