Optimal sorting of neural spikes with wavelet and filtering techniques

Makarov, Valeri A.; Pavlov, Alexey N.; Tupitsyn, Anatoly N.

doi:10.1117/12.769644

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Application Of Wavelet Analysis and Artificial Neural Networ1

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2012

2021

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Cited by 4 publications

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“…Despite the increased time of handling of experimental data, these methods are capable of ensuring a decrease in the identification error, which is main advantage thereof. In particular, the authors of [21] have proposed the parametric wavelet analysis method with adaptive filtration to solve the problem of decrease in the identification error of neural spikes. However, there is reason to assume that a promising solution to the given prob lem is the use of wavelet neural (WNN) techniques [22][23][24][25][26].…”

Section: Application Of Wavelet Analysis and Artificial Neural Networmentioning

confidence: 99%

Application of wavelet analysis and artificial neural networks in solving the problem concerning shape recognition of noisy pulsed signals

Nazimov

Pavlov

2012

J. Commun. Technol. Electron.

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The problem concerning recognition of single pulses under the action of interferences is discussed by the example of classification of neuron action potentials. Joint applications of wavelets and artificial neural networks in solving the the given problem are analyzed. The recognition method, which is based on wavelet neural networks and ensures adjustment of the synapses of a supplementary (''wavelet'') layer, has been pro posed. It is demonstrated that experimental data can efficiently be analyzed via the proposed method.

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