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An Artificial Neural Network Model Based on Neuroscience: Looking Closely at the Brain

  • João Luís Garcia Rosa

Abstract

Classical connectionist models [3, 8, 11] are based upon a simple description of the neuron taking into account the presence of pre-synaptic cells and their synaptic potentials, the activation threshold, and the propagation of an action potential. Certainly, this is an impoverished explanation of human brain characteristics [1, 9, 12]. In this paper, a mechanism to generate a biologically plausible artificial neural network model is presented [10], which is taken to be closer to some of the human brain features. In such a mechanism, the classical framework is redesigned in order to encompass not only the “traditional” features but also labels that model the binding affinities between transmitters and receptors. This is accomplished by a restricted data set, which explains the neural network behavior. In addition to feedforward networks, the present model also contemplates recurrence in its architecture, which allows the system to have re-entrant connections [2].

Keywords

Artificial Neural Network Hide Layer Artificial Neural Network Model Activation Threshold Synaptic Function 
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 Wien 2001

Authors and Affiliations

  • João Luís Garcia Rosa
    • 1
  1. 1.Instituto de InformáticaPUC-CampinasCampinasBrasil

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