Integrating Wavelet Transform Detection with Convolutional Neural Networks for Intelligent Neural Spike Sorting
Keywords:
Neural Spike Sorting, spikes clustering, stationary wavelet, Deep learning, Convolution Neural Networks CNN.Abstract
Neural spike sorting is the basic process of understanding the brain's complex operation, which identifies and classifies spikes or electrical peaks emitted by one neuron. Manual spike sorting methods usually employ manual curation and heuristic algorithms, which can be difficult to use and time-consuming. This method requires a long time and a great deal of expertise. In automatic spike sorting, the spikes generated by various neurons are first detected and then classified automatically. This approach is faster and less labor-intensive than manual spike sorting. In the detection steps the traditional method used is threshold detection but, in this method, there are many spikes can be missed if the threshold high, or many background noise can be detected as spikes if the threshold values so low therefor using wavelet transform methods in detections step more accurate detecting the spikes and make it visualized from the background noise .This article presents a new method involving the joint application of Wavelet Transform Detection and Convolutional Neural Networks (CNNs) in order to expedite the process of intelligent spike sorting. Wavelet transform improves spike detection accuracy in this regard by allowing spikes from background noise to be effectively separated. The CNNs, trained successfully to spike clustering, perform quick and precise classification of the spikes of neurons. Integration of the present techniques will be more precise and effective for the sorting spike of brain supporting the progress in neuroscience studies and brain to the machine interface.
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