图 书 馆
高斯过程神经响应函数的主动学习Active learning of neural response functions with Gaussian processes |
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| 课程网址: | http://videolectures.net/nips2011_park_neuralresponse/ |
| 主讲教师: | Mijung Park |
| 开课单位: | 德克萨斯大学 |
| 开课时间: | 2012-09-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A sizable literature has focused on the problem of estimating a low-dimensional feature space capturing a neuron's stimulus sensitivity. However, comparatively little work has addressed the problem of estimating the nonlinear function from feature space to a neuron's output spike rate. Here, we use a Gaussian process (GP) prior over the infinite-dimensional space of nonlinear functions to obtain Bayesian estimates of the ""nonlinearity"" in the linear-nonlinear-Poisson (LNP) encoding model. This offers flexibility, robustness, and computational tractability compared to traditional methods (e.g., parametric forms, histograms, cubic splines). Most importantly, we develop a framework for optimal experimental design based on uncertainty sampling. This involves adaptively selecting stimuli to characterize the nonlinearity with as little experimental data as possible, and relies on a method for rapidly updating hyperparameters using the Laplace approximation. We apply these methods to data from color-tuned neurons in macaque V1. We estimate nonlinearities in the 3D space of cone contrasts, which reveal that V1 combines cone inputs in a highly nonlinear manner. With simulated experiments, we show that optimal design substantially reduces the amount of data required to estimate this nonlinear combination rule. |
| 关 键 词: | 神经元刺激敏感性; 非线性贝叶斯估计; 高斯 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-14：yxd |
| 最后编审: | 2020-12-15：cjy |
| 阅读次数: | 30 |