Roudi Yasser, Dunn Benjamin, Hertz John
Kavli Institute & Centre for Neural Computation, NTNU, Trondheim, Norway; Nordita, KTH Royal Institute of Technology and Stockholm University, Stockholm, Sweden.
Kavli Institute & Centre for Neural Computation, NTNU, Trondheim, Norway.
Curr Opin Neurobiol. 2015 Jun;32:38-44. doi: 10.1016/j.conb.2014.10.011. Epub 2014 Nov 8.
Our ability to collect large amounts of data from many cells has been paralleled by the development of powerful statistical models for extracting information from this data. Here we discuss how the activity of cell assemblies can be analyzed using these models, focusing on the generalized linear models and the maximum entropy models and describing a number of recent studies that employ these tools for analyzing multi-neuronal activity. We show results from simulations comparing inferred functional connectivity, pairwise correlations and the real synaptic connections in simulated networks demonstrating the power of statistical models in inferring functional connectivity. Further development of network reconstruction techniques based on statistical models should lead to more powerful methods of understanding functional anatomy of cell assemblies.
我们从许多细胞中收集大量数据的能力，与用于从这些数据中提取信息的强大统计模型的发展并驾齐驱。在这里，我们讨论如何使用这些模型来分析细胞集合的活动，重点关注广义线性模型和最大熵模型，并描述一些最近使用这些工具分析多神经元活动的研究。我们展示了模拟结果，比较了模拟网络中推断的功能连接性、成对相关性和实际突触连接，证明了统计模型在推断功能连接性方面的强大作用。基于统计模型的网络重建技术的进一步发展，应该会带来更强大的方法来理解细胞集合的功能解剖结构。
