Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex.

ABSTRACT: Many experimental and computational studies deal with sparseness, balance, and decorrelation in neural networks and explain the presence of these properties as fulfilling requirements related to optimum energy efficiency, network stability, and information representation. These studies leave the question of how these properties arise in the brain unanswered. The present study attempts to address this question using a model built upon the experimentally observed properties of neural responses, homeostasis, and synaptic plasticity. The experimentally observed properties of sparseness, balance, and decorrelation are then expected to emerge from this substrate. A spiking neural model of the primary visual cortex (V1) was investigated. Populations of both inhibitory and excitatory leaky integra

SUBMITTER: Ruslim MA 

PROVIDER: S-EPMC12716757 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature