Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/114296
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Type: Journal article
Title: A Digital Neuromorphic Realization of the 2-D Wilson Neuron Model
Author: Nouri, M.
Hayati, M.
Serrano-Gotarredona, T.
Abbott, D.
Citation: IEEE Transactions on Circuits and Systems II: Express Briefs, 2019; 66(1):136-140
Publisher: IEEE
Issue Date: 2019
ISSN: 1549-7747
1558-3791
Statement of
Responsibility: 
Moslem Nouri, Mohsen Hayati, Teresa Serrano-Gotarredona and Derek Abbott
Abstract: This paper presents a piecewise linear approximation of the nonlinear Wilson (NW) neuron model for the realization of an efficient digital circuit implementation. The accuracy of the proposed piecewise Wilson (PW) model is examined by calculating time domain signal shaping errors. Furthermore, bifurcation analyses demonstrate that the approximation follows the same bifurcation pattern as the nonlinear Wilson (NW) model. As a proof of concept, both models are hardware synthesized and implemented on FPGAs, demonstrating that the PW model has a range of neuronal behaviors similar to the NW model with considerably higher computational performance and a lower hardware overhead. This approach can be used in hardware-based large scale biological neural network simulations and behavioral studies. The mean normalized root mean square error (NRMSE) and maximum absolute error (MAE) of the PW model are 6.32% and 0.31%, respectively, as compared to the NW model.
Keywords: Biological system modeling; mathematical model; neurons; computational modeling; integrated circuit modeling; field programmable gate arrays; hardware
Rights: © 1969, IEEE
RMID: 0030094675
DOI: 10.1109/TCSII.2018.2852598
Appears in Collections:Electrical and Electronic Engineering publications

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