图 书 馆
神经群体中的跳跃经验模型Empirical models of spiking in neural populations |
|
| 课程网址: | http://videolectures.net/nips2011_buesing_neural/ |
| 主讲教师: | Lars Buesing |
| 开课单位: | 伦敦大学学院 |
| 开课时间: | 2012-01-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fitting statistical models to unaveraged data. What statistical structure best describes the concurrent spiking of cells within a local network? We argue that in the cortex, where firing exhibits extensive correlations in both time and space and where a typical sample of neurons still reflects only a very small fraction of the local population, the most appropriate model captures shared variability by a low-dimensional latent process evolving with smooth dynamics, rather than by putative direct coupling. We test this claim by comparing a latent dynamical model with realistic spiking observations to coupled generalised linear spike-response models (GLMs) using cortical recordings. We find that the latent dynamical approach outperforms the GLM in terms of goodness-of-fit, and reproduces the temporal correlations in the data more accurately. We also compare models whose observations models are either derived from a Gaussian or point-process models, finding that the non-Gaussian model provides slightly better goodness-of-fit and more realistic population spike counts. |
| 关 键 词: | 神经元编码; 互联群体; 统计结构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-02-04：nkq |
| 阅读次数: | 42 |