Abstract
Dynamical analysis of complex neural circuits, especially for chaotic nonlinear neural circuits is a difficult task. In this paper, a novel approach to understand the nonlinear dynamic attributes of a neural circuit by using approximate logical model of q-Value Weighted Bounded Operator is discussed, and we proved that if a neural circuit works in a non-chaotic way, a suitable fuzzy logical framework which is an approximate logical model of neural cells can be found and we can analyze or design such kind neural circuit similar to analyze or design a digit computer, but if a neural circuit works in a chaotic way, fuzzy logical frameworks of neural cells are different under different precisions, we should use a multi scale approximate police(see Def.5) for understanding the function of such neural system with arbitrary small precisions.
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© 2008 Springer-Verlag Berlin Heidelberg
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Hu, H., Shi, Z. (2008). Nonlinear Complex Neural Circuits Analysis and Design by q-Value Weighted Bounded Operator. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_24
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DOI: https://doi.org/10.1007/978-3-540-87732-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
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