A Compiler and Simulator for Partial Recursive Functions over Neural Networks

Abstract

The field of Artificial Recurrent Neural Networks (ARNNs), mainly in the last two decades, was able to solve engineering problems while keeping the simplicity of the underlying principles that allow them to mimic their biological counterparts. All this attracts people from many different fields such as Neurophysiology and Computer Science. We introduce our subject from a Computer Science perspective: the ARNN is seen as a computing mechanism able to perform computation based on a program coded as a specific arrangement of neurons and synapses. This work implements a compiler and a simulator based on [4]. In [3,7,5] similar ideas are presented but they are based on higher-level languages. We start by presenting the underlying theoretical context on which this work is based. In section 2 we give a brief review of the concept of partial recursive function. In section 3 we present our approach for building neural networks from partial recursive functions. The explanation of how we adapted the design of [4] into a compiler is given in section 4. Section 5 refers to the simulator and usage examples and section 6 concludes this paper. The simulator is freely available at http://www.di.fc.ul.pt/~jpn/netdef/nwb.html.