ECE/CS/ME 532 Theory and Applications of Pattern Recognition Lab x: Neuronal Spike Sorting
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طبقهبندی پتانسیلهای عمل نرونی با استفاده از شبکههای عصبی شعاعی
Background: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR) of the spikes. The mai...
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Front-end integrated circuits for signal processing are useful in neuronal recording systems that engage a large number of electrodes. Detection, alignment, and sorting of the spike data at the frontend reduces the data bandwidth and enables wireless communication. Without such data reduction, large data volumes need to be transferred to a host computer and typically heavy cables are required w...
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The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a length-N signal x with sparsity level K , M = O(K log(N/K)) random linear projections of x are sufficient to robustly recover x in polynomial time. However, richer models are often applicable in real-...
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Spike sorting is a class of techniques used in the analysis of electrophysiological data. Studying the dynamics of neural activity via electrical recording relies on the ability to detect and sort neural spikes recorded from a number of neurons by the same electrode. This article reviews methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting.
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تاریخ انتشار 2014