Quantization of Map-Based Neuronal Model for Embedded Simulations of Neurobiological Networks in Real-Time

Abstract

The discreet-time (map-based) approach to modeling nonlinear dynamics of spiking and spiking-bursting activity of neurons has demonstrated its very high efficiency in simulations of neuro-biologically realistic behavior both in large-scale network models for brain activity studies and in real-time operation of Central Pattern Generator network models for biomimetic robotics. This paper studies the next step in improving the model computational efficiency that includes quantization of model variables and makes the network models suitable for embedded solutions. We modify a map-based neuron model to enable simulations using only integer arithmetic and demonstrate a significant reduction of computation time in an embedded system using readily available, inexpensive ARM Cortex L4 microprocessors.