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

    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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