Title

Weighted Least-Squares Approach for Identification of a Reduced-Order Adaptive Neuronal Model

Authors

Authors

L. F. Zhi; J. Chen; P. Molnar;A. Behal

Comments

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Abbreviated Journal Title

IEEE Trans. Neural Netw. Learn. Syst.

Keywords

Adaptive spiking behavior; characterization; parameter estimation; quadratic integrate-and-fire; spiking neuron; SPIKING NEURONS; FIRE NEURON; NETWORKS; SIMULATION; INPUT; CELLS; Computer Science, Artificial Intelligence; Computer Science, Hardware &; Architecture; Computer Science, Theory & Methods; Engineering, ; Electrical & Electronic

Abstract

This brief is focused on the parameter estimation problem of a second-order adaptive quadratic neuronal model. First, it is shown that the model discontinuities at the spiking instants can be recast as an impulse train driving the system dynamics. Through manipulation of the system dynamics, the membrane voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parameterized realizable model is then utilized inside a prediction error-based framework to design a dynamic estimator that allows for rapid estimation of model parameters under a persistently exciting input current injection. Simulation results show the feasibility of this approach to predict multiple neuronal firing patterns. Results using both synthetic data (obtained from a detailed ion-channel-based model) and experimental data (obtained from in vitro embryonic rat motoneurons) suggest directions for further work.

Journal Title

Ieee Transactions on Neural Networks and Learning Systems

Volume

23

Issue/Number

5

Publication Date

1-1-2012

Document Type

Article

Language

English

First Page

834

Last Page

840

WOS Identifier

WOS:000303507000014

ISSN

2162-237X

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