New Biomimetic Neural Structures for Artificial Neural Nets

  • Gabriel de Blasio
  • Arminda Moreno-Díaz
  • Roberto Moreno-DíazJr.
  • Roberto Moreno-Díaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6927)

Objectives

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.

Keywords

Lateral Inhibition Presynaptic Inhibition Activation Argument Real Neuron Inhibitory Layer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gabriel de Blasio
    • 1
  • Arminda Moreno-Díaz
    • 2
  • Roberto Moreno-DíazJr.
    • 1
  • Roberto Moreno-Díaz
    • 1
  1. 1.Instituto Universitario de Ciencias y Tecnologías CibernéticasUniversidad de Las Palmas de Gran CanariaSpain
  2. 2.School of Computer ScienceMadrid Technical UniversitySpain

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