A Formal Model for Definition and Simulation of Generic Neural Networks

摘要

This paper presents the definition of a formal data structure, which assists in the characterization of any neural paradigm, with no restriction, including higher-order networks. Within this model, a neural network is mathematically described by specifying some static parameters (number of neurons, order) as well as a set of statistical distributions (which we call the network ‘dynamics’). Once a concrete set of distributions is defined, a single algorithm can simulate any neural paradigm. The presented structure assists in an exhaustive and precise description of the network characteristics and the simulation parameters, providing us with a unified criterion for comparing models and evaluating proposed systems. Though not presented here, the formal model has inspired a software simulator, which implements any system defined according to this structure, thus facilitating the analysis and modelling of neuronal paradigms.