Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80035
Title: Small-signal neural models and their applications
Authors: Basu, Arindam
Keywords: DRNTU::Engineering::Bioengineering
Issue Date: 2012
Source: Basu, A. (2012). Small-signal neural models and their applications. IEEE transactions on biomedical circuits and systems, 6(1), 64-75.
Series/Report no.: IEEE transactions on biomedical circuits and systems
Abstract: This paper introduces the use of the concept of small-signal analysis, commonly used in circuit design, for understanding neural models. We show that neural models, varying in complexity from Hodgkin-Huxley to integrate and fire have similar small-signal models when their corresponding differential equations are close to the same bifurcation with respect to input current. Three applications of small-signal neural models are shown. First, some of the properties of cortical neurons described by Izhikevich are explained intuitively through small-signal analysis. Second, we use small-signal models for deriving parameters for a simple neural model (such as resonate and fire) from a more complicated but biophysically relevant one like Morris-Lecar. We show similarity in the subthreshold behavior of the simple and complicated model when they are close to a Hopf bifurcation and a saddle-node bifurcation. Hence, this is useful to correctly tune simple neural models for large-scale cortical simulations. Finaly, the biasing regime of a silicon ion channel is derived by comparing its small-signal model with a Hodgkin-Huxley-type model.
URI: https://hdl.handle.net/10356/80035
http://hdl.handle.net/10220/16456
DOI: 10.1109/TBCAS.2011.2158314
Rights: © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TBCAS.2011.2158314].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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