Local dendritic balance enables the learning of efficient representations in networks of spiking neurons
Lucas Rudelt,Fabian Mikulasch,Viola Priesemann · COSYNE 2022
How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity only works under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in indivi
Sat, Mar 19, 2022