Advances on criteria for biological plausibility in artificial neural networks: Think of learning processes

Artificial neural network (ANN) community is engaged in biological plausibility issues these days. Different views about this subject can lead to disagreements of classification criteria among ANN researchers. In order to contribute to this debate, two of these views are highlighted here: one is related directly to the cerebral cortex biological structure, and the other focuses the neural features and the signaling between neurons. The model proposed in this paper considers that a biologically more plausible ANN has the purpose to create a more faithful model concerning the biological structure, properties, and functionalities, including learning processes, of the cerebral cortex, not disregarding its computational efficiency. The choice of the models upon which the proposed description is based takes into account two main criteria: the fact they are considered biologically more realistic and the fact they deal with intra and inter-neuron signaling in electrical and chemical synapses. Also, the duration of action potentials is taken into account. In addition to the characteristics for encoding information regarding biological plausibility present in current spiking neuron models, a distinguishable feature is emphasized here: a combination of Hebbian learning and error-driven learning.