图 书 馆
同步记录神经种群的回归线性模型Recurrent linear models of simultaneously-recorded neural populations |
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| 课程网址: | http://videolectures.net/machine_sahani_neural_populations/ |
| 主讲教师: | Maneesh Sahani |
| 开课单位: | 伦敦大学 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Population neural recordings with long-range temporal structure are often best understood in terms of a shared underlying low-dimensional dynamical process. Advances in recording technology provide access to an ever larger fraction of the population, but the standard computational approaches available to identify the collective dynamics scale poorly with the size of the dataset. Here we describe a new, scalable approach to discovering the low-dimensional dynamics that underlie simultaneously recorded spike trains from a neural population. Our method is based on recurrent linear models (RLMs), and relates closely to timeseries models based on recurrent neural networks. We formulate RLMs for neural data by generalising the Kalman-filter-based likelihood calculation for latent linear dynamical systems (LDS) models to incorporate a generalised-linear observation process. We show that RLMs describe motor-cortical population data better than either directly-coupled generalised-linear models or latent linear dynamical system models with generalised-linear observations. We also introduce the cascaded linear model (CLM) to capture low-dimensional instantaneous correlations in neural populations. The CLM describes the cortical recordings better than either Ising or Gaussian models and, like the RLM, can be fit exactly and quickly. The CLM can also be seen as a generalization of a low-rank Gaussian model, in this case factor analysis. The computational tractability of the RLM and CLM allow both to scale to very high-dimensional neural data. |
| 关 键 词: | 递归线性模型; 回归线性模型 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-27：zyk |
| 最后编审: | 2020-12-28：zyk |
| 阅读次数: | 42 |