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一种用于神经突点识别的盲反褶积方法A blind deconvolution method for neural spike identification |
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| 课程网址: | http://videolectures.net/nips2011_ekanadham_deconvolution/ |
| 主讲教师: | Chaitanya Ekanadham |
| 开课单位: | 纽约大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider the problem of estimating neural spikes from extracellular voltage recordings. Most current methods are based on clustering, which requires substantial human supervision and produces systematic errors by failing to properly handle temporally overlapping spikes. We formulate the problem as one of statistical inference, in which the recorded voltage is a noisy sum of the spike trains of each neuron convolved with its associated spike waveform. Joint maximum-a-posteriori (MAP) estimation of the waveforms and spikes is then a blind deconvolution problem in which the coefficients are sparse. We develop a block-coordinate descent method for approximating the MAP solution. We validate our method on data simulated according to the generative model, as well as on real data for which ground truth is available via simultaneous intracellular recordings. In both cases, our method substantially reduces the number of missed spikes and false positives when compared to a standard clustering algorithm, primarily by recovering temporally overlapping spikes. The method offers a fully automated alternative to clustering methods that is less susceptible to systematic errors. |
| 关 键 词: | 细胞; 神经尖峰; 聚类 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-06：lxf |
| 阅读次数: | 51 |