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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Tehran University of Medical Sciences
2011-02-01
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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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1725743098223394816 |