Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA.
Neural Comput. 2013 Feb;25(2):289-327. doi: 10.1162/NECO_a_00398. Epub 2012 Nov 13.
Stimulus from the environment that guides behavior and informs decisions is encoded in the firing rates of neural populations. Neurons in the populations, however, do not spike independently: spike events are correlated from cell to cell. To what degree does this apparent redundancy have an impact on the accuracy with which decisions can be made and the computations required to optimally decide? We explore these questions for two illustrative models of correlation among cells. Each model is statistically identical at the level of pairwise correlations but differs in higher-order statistics that describe the simultaneous activity of larger cell groups. We find that the presence of correlations can diminish the performance attained by an ideal decision maker to either a small or large extent, depending on the nature of the higher-order correlations. Moreover, although this optimal performance can in some cases be obtained using the standard integration-to-bound operation, in others it requires a nonlinear computation on incoming spikes. Overall, we conclude that a given level of pairwise correlations, even when restricted to identical neural populations, may not always indicate redundancies that diminish decision-making performance.
环境刺激引导行为并为决策提供信息，这些信息被编码在神经元群体的发放频率中。然而，神经元群体中的神经元并不是独立放电的：细胞之间的放电事件是相关的。这种明显的冗余程度对决策的准确性和做出最优决策所需的计算有多大影响？我们针对两种细胞间相关性的说明性模型探讨了这些问题。每个模型在成对相关性方面在统计上是相同的，但在描述更大细胞群体同时活动的高阶统计方面有所不同。我们发现，相关性的存在可能会在很大或很小程度上降低理想决策者的表现，具体取决于高阶相关性的性质。此外，尽管在某些情况下，使用标准的积分到边界操作可以获得这种最优性能，但在其他情况下，它需要对传入的尖峰进行非线性计算。总的来说，我们得出结论，即使在限制于相同神经元群体的情况下，给定水平的成对相关性也不一定表示会降低决策表现的冗余。
