A Constrained Linear Approach To Identify A Multi-Timescale Adaptive Threshold Neuronal Model

Abstract

This paper is focused on the parameter estimation problem of a multi-timescale adaptive threshold (MAT) neuronal model. Using the dynamics of a non-resetting leaky integrator equipped with an adaptive threshold, a constrained iterative linear least squares method is implemented to fit the model to the reference data. Through manipulation of the system dynamics, the threshold voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parametrized realizable model is then utilized inside a prediction error based framework to identify the threshold parameters with the purpose of predicting single neuron precise firing times. This estimation scheme is evaluated using both synthetic data obtained from an exact model as well as the experimental data obtained from in vitro rat somatosensory cortical neurons. Results show the ability of this approach to fit the MAT model to different types of reference data.

Publication Date

12-2-2015

Publication Title

2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2015

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCABS.2015.7344704

Socpus ID

84960877250 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84960877250

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