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Foundation and Classification of Nonconventional Neural Units and Paradigm of Nonsynaptic Neural Interaction

Foundation and Classification of Nonconventional Neural Units and Paradigm of Nonsynaptic Neural Interaction

I. Bukovsky, J. Bila, M. M. Gupta, Z. G. Hou, N. Homma
Copyright: © 2010 |Pages: 16
ISBN13: 9781605669021|ISBN10: 1605669024|ISBN13 Softcover: 9781616924140|EISBN13: 9781605669038
DOI: 10.4018/978-1-60566-902-1.ch027
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MLA

Bukovsky, I., et al. "Foundation and Classification of Nonconventional Neural Units and Paradigm of Nonsynaptic Neural Interaction." Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence, edited by Yingxu Wang, IGI Global, 2010, pp. 508-523. https://doi.org/10.4018/978-1-60566-902-1.ch027

APA

Bukovsky, I., Bila, J., Gupta, M. M., Hou, Z. G., & Homma, N. (2010). Foundation and Classification of Nonconventional Neural Units and Paradigm of Nonsynaptic Neural Interaction. In Y. Wang (Ed.), Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence (pp. 508-523). IGI Global. https://doi.org/10.4018/978-1-60566-902-1.ch027

Chicago

Bukovsky, I., et al. "Foundation and Classification of Nonconventional Neural Units and Paradigm of Nonsynaptic Neural Interaction." In Discoveries and Breakthroughs in Cognitive Informatics and Natural Intelligence, edited by Yingxu Wang, 508-523. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-902-1.ch027

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Abstract

This chapter introduces basic types of nonconventional neural units and focuses their mathematical notation and classification. Namely, the notation and classification of higher-order nonlinear neural units, time-delay dynamic neural units, and time-delay higher-order nonlinear neural units is introduced. The classification of nonconventional neural units is founded first according to nonlinearity of aggregating function, second according to the dynamic order, third according to time-delay implementation within neural units. Introduction into the simplified parallel of the higher-order nonlinear aggregating function of higher-order neural units revealing both the synaptic and nonsynaptic neural interaction is made; thus, a new parallel between the mathematical notation of nonconventional neural units and the neural signal processing of biological neurons and is drawn. Based on the mathematical notation of neural input inter-correlations of higher-order neural units, it is shown that higher-order polynomial aggregating function of neural inputs can be naturally understood as a single-equation representation consisting of synaptic neural operation plus nonsynaptic neural operation. Thus it unravels new simplified yet universal mathematical insight into understanding the higher computational power of neurons that also conforms to biological neuronal morphology according to nowadays achievements of biomedical sciences.

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