An Artificial Neural Network Model Based on Neuroscience: Looking Closely at the Brain
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 , 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 .
KeywordsArtificial Neural Network Hide Layer Artificial Neural Network Model Activation Threshold Synaptic Function
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