Time-varying neural dynamics system identification method based on chebyshev polynomial expansion

Abstract

The present invention puts forward a time-varying neural dynamics system identification method based on chebyshev polynomial expansion. The time-varying neural dynamics system identification method comprises the steps of: representing a time-varying neural system consisting of simulation input / output spike potential sequences with a Volterra series, and representing feedforward and feedback kernel functions with different Volterra kernels; expanding the time-varying Volterra kernel with a Laguerre primary function to obtain a time-varying generalized Laguerre-Volterra model; expanding time-varying parameters of the time-varying generalized Laguerre-Volterra model with a chebyshev polynomial, so as to convert the time-varying model into a time-invariant model; and selecting significant model items by using a forward orthogonal regression algorithm, estimating time-invariant parameters by using a generalized linear fitting algorithm, and then obtaining the time-varying parameters and the original time-varying kernel functions through reverse solution. Compared with an adaptive filtering technology in the prior art, the time-varying neural dynamics system identification method of the present invention has a better tracking capability for a strong non-stationary neural system signal, can achieve accurate tracking for the time-varying system kernel functions, can achieve modeling for the neural system, especially provides a new research method for system modeling of massive high-dimensional data, and has an important meaning for revealing a complex neural dynamics mechanism for completing information processing by a cerebrum.

Description

technical field [0001] The invention provides a time-varying neurodynamic system identification algorithm based on Chebyshev polynomial expansion, which provides a new analysis method for time-varying system identification facing spike sequence signals, and belongs to the field of system identification. Background technique [0002] The nervous system is a dynamic system, and the underlying mechanism of neuron spike activity exhibits time-varying characteristics. This time-varying may be extremely slow, but its changes cannot be ignored as time accumulates. Therefore, using a time-invariant model to analyze the potential mechanism of neuron spiking activity obviously cannot obtain long-term stable and reliable results. Analyzing the potential time-varying laws of neurons, and developing time-varying system modeling and identification applications of neuron spike sequences have gradually attracted the attention of researchers. [0003] Most of the time-varying system modelin...

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Problems solved by technology

However, if the parameters of the time-varying system change too fast, the estimation of the parameters of the time-varying system will be delayed due to the convergence defect of the adaptive algorithm.

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