Center for Neural Science and Center for Data Science, New York University, 10003 New York, USA; IST Austria, A-3400 Klosterneuburg, Austria.
IST Austria, A-3400 Klosterneuburg, Austria.
Curr Opin Neurobiol. 2017 Oct;46:120-126. doi: 10.1016/j.conb.2017.08.001. Epub 2017 Sep 3.
Neural responses are highly structured, with population activity restricted to a small subset of the astronomical range of possible activity patterns. Characterizing these statistical regularities is important for understanding circuit computation, but challenging in practice. Here we review recent approaches based on the maximum entropy principle used for quantifying collective behavior in neural activity. We highlight recent models that capture population-level statistics of neural data, yielding insights into the organization of the neural code and its biological substrate. Furthermore, the MaxEnt framework provides a general recipe for constructing surrogate ensembles that preserve aspects of the data, but are otherwise maximally unstructured. This idea can be used to generate a hierarchy of controls against which rigorous statistical tests are possible.
神经反应具有高度的结构性，群体活动仅限于可能的活动模式的一小部分天文范围内。描述这些统计规律对于理解电路计算很重要，但在实践中具有挑战性。在这里，我们回顾了基于最大熵原理的最近方法，该方法用于量化神经活动中的集体行为。我们强调了最近的模型，这些模型捕获了神经数据的群体统计信息，为神经编码的组织及其生物基质提供了深入的见解。此外，最大熵框架为构建替代集合提供了一个通用的方法，这些集合保留了数据的某些方面，但在其他方面则最大程度地无结构。这个想法可用于生成一个控制层次结构，以便对其进行严格的统计测试。
