Local dendritic balance enables learning of efficient representations in networks of spiking neurons.

ABSTRACT: 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 works only 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 individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex hig

SUBMITTER: Mikulasch FA 

PROVIDER: S-EPMC8685685 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature