Abstract
Signal Processing for Neural Spike Trains
Highlights
(1) Recording and Analysis of Neuronal Spikes from Single Neuron
Because of its strength of isolating activity related to the muscle length, this algorithm has a potential application for the closed-loop functional electrical stimulation (FES) system with natural sensory feedback
The paper “State-space algorithms for Computational Intelligence and Neuroscience estimating spike rate functions” by Smith et al addresses a fundamental question of determining changes in activity in neural firing, and the authors propose a state-space model to estimate the spike rate function and compare their approach with the established Bayesian adaptive regression splines (BARSs) algorithm and a cubic spline smoothing algorithm
Summary
(1) Recording and Analysis of Neuronal Spikes from Single Neuron. The first paper “Quantitative estimation of the non-stationary behavior of neural spontaneous activity” by Destro-Filho et al describes a quantitative approach to estimate the nonstationary behavior of neuronal spontaneous activity. Editorial Signal Processing for Neural Spike Trains Oweiss,4 Rodrigo Quian Quiroga,5 and Nitish V.
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