Multi-wavelet-basis function expansion-based accurate identification method of spike-potential time-varying Granger causality (GC)

Abstract

The invention provides a multi-wavelet-basis function expansion-based accurate identification method of spike-potential time-varying Granger causality (GC), and belongs to the technical field of signal analysis and processing. As shown in FIG.1, the method includes: firstly, using an AIC (Akaike information criterion) method to select optimal memory length corresponding to each neuron; then establishing a generalized L-V (Laguerre-Volterra) model, and using a multi-wavelet-basis function method to expand the same to obtain a time-invariant parameter model; then carrying out sparsification on the expanded-formula model through an OFR algorithm, estimating sparse model parameters, and inversely reconstructing a time-varying kernel function in the generalized L-V model; and finally, carryingout solving of logarithmic likelihood values of a model point process, and calculating final time-varying Granger causality values of the corresponding neurons. Compared with existing SSPPF (stochastic state point process filter)-based time-varying Granger estimation method, the method provided by the invention can better track fast-changing causality relationships, improves time-varying causalityrecognition accuracy, and provides a theoretical calculation framework and a new solution for neuron spike-potential time-varying function connection identification.

Description

technical field [0001] The present invention proposes a spike time-varying Granger causality accurate identification algorithm based on multi-wavelet basis function expansion, which provides a new solution for the spike sequence-oriented time-varying Granger causality accurate identification, and belongs to signal analysis and processing technology field. Background technique [0002] Neuronal spikes in the nervous system exhibit cluster connection characteristics, and neural clusters are interconnected and functionally similar collections of neurons. Identifying neuronal functional connectivity is a necessary step in understanding how neurons in brain regions organize to represent, transmit, process information and further perform higher cognitive functions. The nervous system is a dynamic system, neuron synapses have plasticity, and the functional connection relationship between neurons shows time-varying characteristics. Therefore, if the time-invariant method is used to...

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Problems solved by technology

However, SSPPF requires a large number of iterative processes to track accurate time-varying parameters. For time-varying parameters that change rapidly in time-varying systems, the slow convergence speed of the algorithm leads to poor estimation performance of time-varying parameters. Therefore, based on this type of Inaccurate results of time-varying Granger causality identification method for adaptive filtering algorithm with slow convergence

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