Feature, configuration, history: A bio-inspired framework for information representation in neural networks

Abstract

Artificial Neural Networks are very efficient adaptive models but one of their recognized weaknesses is about information representation, often carried out in an input vector without a structure. Beyond the classical elaboration of a hierarchical representation in a series of layers, we report here inspiration from neuroscience and argue for the design of heterogenous neural networks, processing information at feature, configuration and history levels of granularity, and interacting very efficiently for high-level and complex decision making. This framework is built from known characteristics of the sensory cortex, the hippocampus and the prefrontal cortex and is exemplified here in the case of pavlovian conditioning, but we propose that it can be advantageously applied in a wider extent, to design flexible and versatile information processing with neuronal computation.