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A Formal Model for Definition and Simulation of Generic Neural Networks

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Abstract

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.

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Atencia, M.A., Joya, G. & Sandoval, F. A Formal Model for Definition and Simulation of Generic Neural Networks. Neural Processing Letters 11, 87–105 (2000). https://doi.org/10.1023/A:1009678528953

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