The importance of mixed selectivity in complex cognitive tasks.
The importance of mixed selectivity in complex cognitive tasks.
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前额叶皮层（PFC）中的单个神经元活动被调整为多个任务相关方面的混合物。这种混合的选择性是高度异质的，似乎是无序的，因此难以解释。我们分析了记录在猴子的神经活动在一个对象序列记忆任务，以确定的混合选择性在subserving归因于PFC的认知功能的作用。我们发现，混合选择性神经元编码分布式信息的所有任务相关的方面。每个方面都可以从神经元群体中解码，即使消除了对该方面的单细胞选择性。此外，混合选择性提供了一个显着的计算优势，专门的反应方面的输入输出功能的剧目可实现的读出神经元。这种优势源于对任务相关变量的混合物的高度多样化的非线性选择性，这是高维神经表征的特征。至关重要的是，这个维度是动物行为的预测，因为它在错误试验中崩溃。我们的研究结果表明，将注意力的焦点从表现出易于解释的反应调谐的神经元转移到广泛观察到的，但很少分析的混合选择性神经元。
Single-neuron activity in prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input-output functions implementable by readout neurons. This advantage originates from the highly diverse non-linear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings suggest to move the focus of attention from neurons that exhibit easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons.