Neurohydrodynamics as a heuristic mechanism for cognitive processes in decision-making

Hardy, Leon; Liu, Dahai; Levine, Daniel S.

doi:10.1109/ijcnn.2012.6252375

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…6) Model (1) often represents biological systems, which reflects the survival and perdition of species. In contrast, (4) stems from engineering applications, and in a similar manner to Hopfield neural network model, they can be used in fields, such as optimization, decision making and learning [91], [208], [252], [253], and signal processing [327]. The similarities between ( 1) and ( 4) are as follows: 1) the model structure in mathematical description is the same and 2) the symmetry requirements of the interconnection matrices are the same in the early days of neural network stability theory.…”

Section: Stability Problems For Two Classes Of Cohen-grossberg Neural...mentioning

confidence: 99%

A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks

Zhang¹,

Wang²,

Liu

2014

IEEE Trans. Neural Netw. Learning Syst.

577

151

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Stability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail. For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized. The relationship among stability results in different forms, such as algebraic inequality forms, M-matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared. Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed. Concluding remarks and future directions of stability analysis of recurrent neural networks are given.

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