图 书 馆
大神经种群中的信息速率与最优译码Information Rates and Optimal Decoding in Large Neural Populations |
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| 课程网址: | http://videolectures.net/nips2011_pfau_decoding/ |
| 主讲教师: | David Pfau |
| 开课单位: | 哥伦比亚大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many fundamental questions in theoretical neuroscience involve optimal decoding and the computation of Shannon information rates in populations of spiking neurons. In this paper, we apply methods from the asymptotic theory of statistical inference to obtain a clearer analytical understanding of these quantities. We find that for large neural populations carrying a finite total amount of information, the full spiking population response is asymptotically as informative as a single observation from a Gaussian process whose mean and covariance can be characterized explicitly in terms of network and single neuron properties. The Gaussian form of this asymptotic sufficient statistic allows us in certain cases to perform optimal Bayesian decoding by simple linear transformations, and to obtain closed-form expressions of the Shannon information carried by the network. One technical advantage of the theory is that it may be applied easily even to non-Poisson point process network models; for example, we find that under some conditions, neural populations with strong history-dependent (non-Poisson) effects carry exactly the same information as do simpler equivalent populations of non-interacting Poisson neurons with matched firing rates. We argue that our findings help to clarify some results from the recent literature on neural decoding and neuroprosthetic design. |
| 关 键 词: | 大神经种群; 神经元; 诊断分析 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-29：yxd |
| 最后编审: | 2020-12-29：yxd |
| 阅读次数: | 42 |