文章基本信息

标题：Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR
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作者：Bernard P. Zeigler ; Alexandre Muzy
期刊名称：Systems
电子版ISSN：2079-8954
出版年度：2017
卷号：5
期号：1
页码：7
DOI：10.3390/systems5010007
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：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