A biological-like controller using improved spiking neural networks

Abstract

The replication of the behavior of biological systems is a primary step to understand how the brain and the neural networks that comprise it work. The study of these systems began with the analysis of animals with low neural complexity as well as with small control neural networks such as those present in the reflex acts of the human body. However, the study of the brain, due to its intricate structure, still presents many unknowns and challenges to be solved. Meanwhile, one way to understand how biological systems work is to emulate their behavior through computer simulations. Artificial neural networks (ANNs) offered the opportunity to replicate neural structures to understand and reproduce their behavior and performance. Many types of ANNs are based on the use of activation functions. However, these ANNs are simplified models that do not replicate accurately the behavior of complex biological neural systems. For this reason, spike-based models were developed to reproduce real biological systems more faithfully. This work proposes simulating the motor behavior of the central nervous system to control the position of an arm. To this end, a spiking neural network has been developed to emulate motion control by means of a fixed structure that reproduces reflex arcs. A channel-based synapse model is proposed to improve the biological similarity of the controller. Finally, based on the equilibrium point hypothesis, a control scheme capable of reaching speeds and response times similar to those of a human has been designed. Furthermore, the developed controller has been endowed with learning capabilities thanks to the reproduction of the synapse plasticity process that takes place in real biological systems. The performance of the proposed approach has been demonstrated by simulating the control of the movement of an arm using the Hill’s Muscle Model.