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Systems 2017, 5(1), 7; doi:10.3390/systems5010007

Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR

1
Co-Director of the Arizona Center for Integrative Modeling and Simulation (ACIMS), University of Arizona and Chief Scientist, RTSync Corp., 12500 Park Potomac Ave. #905-S, Potomac, MD 20854, USA
2
CNRS, I3S, Université Côte d’Azur, 06900 Sophia Antipolis, France
*
Author to whom correspondence should be addressed.
Academic Editors: Gianfranco Minati, Eliano Pessa and Ignazio Licata
Received: 17 November 2016 / Revised: 15 January 2017 / Accepted: 18 January 2017 / Published: 25 January 2017
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Abstract

In the context of the modeling and simulation of neural nets, we formulate definitions for the behavioral realization of memoryless functions. The definitions of realization are substantively different for deterministic and stochastic systems constructed of neuron-inspired components. In contrast to earlier generations of neural net models, third generation spiking neural nets exhibit important temporal and dynamic properties, and random neural nets provide alternative probabilistic approaches. Our definitions of realization are based on the Discrete Event System Specification (DEVS) formalism that fundamentally include temporal and probabilistic characteristics of neuron system inputs, state, and outputs. The realizations that we construct—in particular for the Exclusive Or (XOR) logic gate—provide insight into the temporal and probabilistic characteristics that real neural systems might display. Our results provide a solid system-theoretical foundation and simulation modeling framework for the high-performance computational support of such applications. View Full-Text
Keywords: neural nets; spiking neurons; xor problem; input/output system; system specifications; Discrete Event System Specification neural nets; spiking neurons; xor problem; input/output system; system specifications; Discrete Event System Specification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zeigler, B.P.; Muzy, A. Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR. Systems 2017, 5, 7.

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