Specialization within cortical models : An application to causality learning
Abstract
In this paper we present the principle of learning by specialization within a cortically-inspired framework. Specialization of neurons in the cortex has been observed, and many models are using such "cortical-like" learning mechanisms, adapted for computational efficiency. Adaptations will be discussed, in light of experiments with our cortical model addressing causality learning from perceptive sequences.