Nonlinear mixed selectivity supports reliable neural computation
Nonlinear mixed selectivity supports reliable neural computation
复制标题
DOI：
10.1371/journal.pcbi.1007544
复制
发表时间：
2020-02-01
影响因子：
4.3
通讯作者：
Freedman, David J.
中科院分区：
文献类型：
--
作者：
Johnston, W. Jeffrey;Palmer, Stephanie E.;Freedman, David J.
大脑中的神经元活动是可变的，但感知和行为通常都是可靠的。大脑是如何实现这一目标的呢？在这里，我们表明，大脑可以使用多种刺激特征的联合编码（通常称为非线性混合选择性）来支持使用不可靠神经元的可靠信息传输。在初级感觉、决策和运动大脑区域中观察到非线性混合特征表示。在这些区域中，不同的特征几乎总是在某种程度上非线性混合，而不是单独表示或仅使用加性（线性）混合，我们将其称为纯选择性。混合选择性先前已被证明可以支持复杂行为任务的灵活线性解码。在这里，我们证明它还有另一个重要的好处：在许多情况下，即使两种形式的选择性使用相同数量的尖峰，它也比纯选择性减少几个数量级的解码错误。这种好处适用于感觉、运动和更抽象的认知表征。此外，我们的实验证据表明，即使大脑无法实现行为上有用的线性解码，也存在混合选择性。这表明非线性混合选择性可能是大脑用来进行可靠且高效的神经计算的通用编码方案。
Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinearly mixed feature representations have been observed throughout primary sensory, decision-making, and motor brain areas. In these areas, different features are almost always nonlinearly mixed to some degree, rather than represented separately or with only additive (linear) mixing, which we refer to as pure selectivity. Mixed selectivity has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that it has another important benefit: in many cases, it makes orders of magnitude fewer decoding errors than pure selectivity even when both forms of selectivity use the same number of spikes. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that mixed selectivity exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that nonlinear mixed selectivity may be a general coding scheme exploited by the brain for reliable and efficient neural computation.