Adaptive Synaptogenesis Constructs Neural Codes That Benefit Discrimination.

ABSTRACT: Intelligent organisms face a variety of tasks requiring the acquisition of expertise within a specific domain, including the ability to discriminate between a large number of similar patterns. From an energy-efficiency perspective, effective discrimination requires a prudent allocation of neural resources with more frequent patterns and their variants being represented with greater precision. In this work, we demonstrate a biologically plausible means of constructing a single-layer neural network that adaptively (i.e., without supervision) meets this criterion. Specifically, the adaptive algorithm includes synaptogenesis, synaptic shedding, and bi-directional synaptic weight modification to produce a network with outputs (i.e. neural codes) that represent input patterns proportional to the

SUBMITTER: Thomas BT 

PROVIDER: S-EPMC4503424 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature