Impact of Correlated Neural Activity on Decision-Making Performance
Impact of Correlated Neural Activity on Decision-Making Performance
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DOI：
10.1162/neco_a_00398
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发表时间：
2013-02-01
影响因子：
2.9
通讯作者：
Shea-Brown, Eric
中科院分区：
文献类型：
--
作者：
Cain, Nicholas;Shea-Brown, Eric
来自环境的刺激引导行为并通知决策，编码在神经群体的放电率中。然而，群体中的神经元并不是独立的尖峰事件：尖峰事件在细胞之间是相关的。这种明显的冗余在多大程度上影响了做出决策的准确性和最佳决策所需的计算？我们用两个细胞间相关性的说明性模型来探讨这些问题。每个模型在两两相关的水平上在统计上是相同的，但在描述较大细胞组同时活动的高阶统计量上不同。我们发现，相关性的存在会在很小程度上或很大程度上降低理想决策者的绩效，这取决于高阶相关性的性质。此外，尽管在某些情况下可以使用标准的积分到界限运算来获得这种最佳性能，但在其他情况下，它需要对输入尖峰进行非线性计算。总体而言，我们得出的结论是，给定水平的两两相关，即使限制在相同的神经群体中，也可能并不总是表明会降低决策绩效的冗余。
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.