Guillaume Bellec

The paper develops a biologically informed recurrent neural network to predict optogenetic perturbations in cortical circuits, showing improved performance over generic models.

This paper shows that staggered critical periods improve local learning in a visual processing model, enhancing representation quality and efficiency.

This paper shows that biologically realistic spiking neural networks can learn from few examples by storing priors in synaptic weights and using network state for rapid information absorption.

The paper shows spike frequency adaptation improves spiking neural network performance on temporally dispersed tasks, reaching human brain levels.

This paper introduces a new learning method for spiking neural networks that enables biologically plausible online learning through gradient descent, achieving performance comparable to BPTT.