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)


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.


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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  1. 1.
    McCulloch, W.S., Pitts, W.H.: A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Byophysics 5, 115–133 (1943)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Blum, M.: Properties of a Neuron with Many Inputs. In: Foerster, V., Zopf, R. (eds.) Principles of Self organitation, pp. 95–119. Pergamon Press, New York (1961)Google Scholar
  3. 3.
    Schipperheyn, J.J.: Contrast Detection in Frog’s Retina. Acta Physiol. Pharmacol. Neerlandica 13, 231–277 (1965)Google Scholar
  4. 4.
    Abbott, L.F., Regehr, W.G.: Synaptic Computation. Nature 431, 796–803 (2004)CrossRefGoogle Scholar
  5. 5.
    Venkataramani, S., Taylor, W.R.: Orientation Selectivity in Rabbit Retinal Ganglion Cells is Mediated by Presynaptic Inhibition. The Journal of Neuroscience 30(46), 15664–15676 (2010)CrossRefGoogle Scholar
  6. 6.
    McCulloch, W.S., Papert, S.A., Blum, M., da Fonseca, J.S., Moreno-Díaz, R.: The Fun of Failures. Annals of the New York Academy of Sciences 156(2), 963–968 (1969)CrossRefGoogle Scholar
  7. 7.
    Moreno-Díaz, R.: Deterministic and Probabilistic Neural Nets with Loops. Math. Biosciences 11, 129–131 (1971)CrossRefzbMATHGoogle Scholar
  8. 8.
    Moreno-Díaz, R., de Blasio, G., Moreno-Díaz, A.: A Framework for Modelling Competitive and Cooperative Computation in Retinal Processing. In: Ricciardi, L.M., Buonocuore, A., Pirozzi, E. (eds.) Collective Dynamics: Topics on Competition and Cooperation in the Biosciences, pp. 88–97. American Institute of Physics, New York (2008)Google Scholar
  9. 9.
    Segev, I.: What do Dendrites and their Synapses Tell the Neuron? J. Neurophysiol 95, 1295–1297 (2006)CrossRefGoogle Scholar
  10. 10.
    London, M., Häusser, M.: Dendritic Computation. Annu. Rev. Neurosci. 28, 503–532 (2005)CrossRefGoogle Scholar
  11. 11.
    Moreno Díaz Jr., R.: Computación Paralela y Distribuida: Relaciones Estructura-Función en Retinas. Phd. Thesis, Universidad de Las Palmas de G.C (1993)Google Scholar
  12. 12.
    Moreno-Díaz, R., de Blasio, G.: Systems Methods in Visual Modelling. Sistems Analysis Modelling Simulation 43(9), 1159–1171 (2003)CrossRefGoogle Scholar

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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