New biomimetic neural structures for artificial neural nets

Abstract

The general aim is to formalize known properties of real neurons, formulating them into appropriate mathematical models. These will converge into, hopefully, more powerful neurophysiological founded distributed computation units of artificial neural nets. Redundancy and distributed computation are key factors to be embodied in the corresponding biomimetic structures. We focus in two neurophysiological processes: first, the dendro-dendritic or afferent non linear interactions, prior to the synapses with the cell body. Computational redundancy (and reliability as a consequence) is to be expected. Second, distributed computation, also provoked by a dendritic-like computational structure to generate arbitrary receptive fields weights or profiles, where also, a kind of reliability is expected, result of the distributed nature of the computation.