Neuronal spike sorting based on radial basis function neural networks

"nBackground: 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, sorti...

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Main Authors: Taghavi Kani M, Homayoon Jafari A, Khoshnevisan A, Arabalibeyk H, Abolhasani MJ
Format: Article
Language:fas
Published: Tehran University of Medical Sciences 2011-02-01
Series:Tehran University Medical Journal
Subjects:
Online Access:http://journals.tums.ac.ir/PdfMed.aspx?pdf_med=/upload_files/pdf/17516.pdf&manuscript_id=17516
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spelling doaj-065c56b61a1145dda890e581c636c21e2020-11-24T22:29:48ZfasTehran University of Medical SciencesTehran University Medical Journal1683-17641735-73222011-02-016811638643Neuronal spike sorting based on radial basis function neural networksTaghavi Kani MHomayoon Jafari AKhoshnevisan AArabalibeyk HAbolhasani MJ"nBackground: 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 main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF) neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS) reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.http://journals.tums.ac.ir/PdfMed.aspx?pdf_med=/upload_files/pdf/17516.pdf&manuscript_id=17516Extracellularneural networkneuroengineeringneuronal spike sortingradial basis functionrecording
collection DOAJ
language fas
format Article
sources DOAJ
author Taghavi Kani M
Homayoon Jafari A
Khoshnevisan A
Arabalibeyk H
Abolhasani MJ
spellingShingle Taghavi Kani M
Homayoon Jafari A
Khoshnevisan A
Arabalibeyk H
Abolhasani MJ
Neuronal spike sorting based on radial basis function neural networks
Tehran University Medical Journal
Extracellular
neural network
neuroengineering
neuronal spike sorting
radial basis function
recording
author_facet Taghavi Kani M
Homayoon Jafari A
Khoshnevisan A
Arabalibeyk H
Abolhasani MJ
author_sort Taghavi Kani M
title Neuronal spike sorting based on radial basis function neural networks
title_short Neuronal spike sorting based on radial basis function neural networks
title_full Neuronal spike sorting based on radial basis function neural networks
title_fullStr Neuronal spike sorting based on radial basis function neural networks
title_full_unstemmed Neuronal spike sorting based on radial basis function neural networks
title_sort neuronal spike sorting based on radial basis function neural networks
publisher Tehran University of Medical Sciences
series Tehran University Medical Journal
issn 1683-1764
1735-7322
publishDate 2011-02-01
description "nBackground: 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 main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF) neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS) reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
topic Extracellular
neural network
neuroengineering
neuronal spike sorting
radial basis function
recording
url http://journals.tums.ac.ir/PdfMed.aspx?pdf_med=/upload_files/pdf/17516.pdf&manuscript_id=17516
work_keys_str_mv AT taghavikanim neuronalspikesortingbasedonradialbasisfunctionneuralnetworks
AT homayoonjafaria neuronalspikesortingbasedonradialbasisfunctionneuralnetworks
AT khoshnevisana neuronalspikesortingbasedonradialbasisfunctionneuralnetworks
AT arabalibeykh neuronalspikesortingbasedonradialbasisfunctionneuralnetworks
AT abolhasanimj neuronalspikesortingbasedonradialbasisfunctionneuralnetworks
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