Perceptron-like computation based on biologically-inspired neurons with heterosynaptic mechanisms

Perceptrons are one of the fundamental paradigms in artificial neural networks and a keyprocessing scheme in supervised classification tasks. However, the algorithm they provideis given in terms of unrealistically simple processing units and connections andtherefore, its implementation in real neural networks is hard to be fulfilled. In thiswork, we present a neural circuit able to perform perceptron’s computation based onrealistic models of neurons and synapses. The model uses Wang-Buzsáki neurons withcoupling provided by axodendritic and axoaxonic synapses (heterosynapsis). The maincharacteristics of the feedforward perceptron operation are conserved, which allows tocombine both approaches: whereas the classical artificial system can be used to learn aparticular problem, its solution can be directly implemented in this neural circuit. As aresult, we propose a biologically-inspired system able to work appropriately in a widerange of frequencies and system parameters, while keeping robust to noise and error.