Empirical models of spiking in neural populations
Empirical models of spiking in neural populations
复制标题
DOI：
--
复制
发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
J. Macke;Lars Buesing;J. Cunningham;Byron M. Yu;K. Shenoy;M. Sahani
中科院分区：
文献类型：
--
作者：
J. Macke;Lars Buesing;J. Cunningham;Byron M. Yu;K. Shenoy;M. Sahani
新皮层中的神经元作为局部互联群体的一部分进行编码和计算。大规模的多电极记录使得通过拟合统计模型到非平均数据来经验地访问这些人口过程成为可能。哪种统计结构最能描述本地网络中单元的并发尖峰？我们认为，在皮层中，放电在时间和空间上都表现出广泛的相关性，而典型的神经元样本仍然只反映了当地人口的一小部分，最合适的模型通过低维潜在过程的平滑动态演变来捕获共享变异性，而不是通过假定的直接耦合。我们通过比较具有实际尖峰观测的潜在动力学模型与使用皮质记录的耦合广义线性尖峰响应模型（GLMs）来验证这一说法。我们发现潜在动态方法在拟合优度方面优于GLM，并且更准确地再现了数据中的时间相关性。我们还比较了观察模型来源于高斯模型或点过程模型的模型，发现非高斯模型提供了更好的拟合优度和更真实的人口峰值计数。
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.