کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4336633 | 1295220 | 2008 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Classification of neuronal spikes over the reconstructed phase space
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب (عمومی)
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چکیده انگلیسی
Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Neuroscience Methods - Volume 168, Issue 1, 15 February 2008, Pages 203-211
Journal: Journal of Neuroscience Methods - Volume 168, Issue 1, 15 February 2008, Pages 203-211
نویسندگان
Hsiao-Lung Chan, Tony Wu, Shih-Tseng Lee, Shih-Chin Fang, Pei-Kuang Chao, Ming-An Lin,