Sparse connectivity enables efficient information processing in cortex-like artificial neural networks.

ABSTRACT: Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse connectivity for a network's function? Surprisingly, it has been shown that sparse connectivity impairs information processing in artificial neural networks (ANNs). Does this imply that sparse connectivity also impairs information processing in biological neural networks? Although ANNs were originally inspired by the brain, conventional ANNs differ substantially in their structural network architecture from cortical networks. To disentangle the relevance of these structural properties for information processing in networks, we systematically constructed ANNs constrained by interpretable features of c

SUBMITTER: Fruengel R 

PROVIDER: S-EPMC11966417 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature