Paper Title
NEURAL NETWORKS FOR LOCOMOTION IN ROBOTS, AND DIGITAL COUNTERS

Abstract
This document presents the simulation of an Artificial Neural Network implementing a digital counter which could be used as a Central Pattern Generator (CPG) for locomotion in robots. The counter was designed by using an implementation of T flip-flops and NAND gates with neurons. Inhibitory and excitatory neurons were used for this simulation employing a leaky integrate-and-fire (LIF) neuronal model. The digital counter has an output of three bits, but it was designed to be scalable so that if more “muscles” (motors) need to be connected, the counter can be modified to have more output bits. Keywords - Artificial neural networks, Central Pattern Generator, locomotion in robots, Python.