Department of Psychology, University of Pennsylvania , Philadelphia, Pennsylvania.
J Neurophysiol. 2019 Jan 1;121(1):115-130. doi: 10.1152/jn.00503.2018. Epub 2018 Nov 7.
Task performance is determined not only by the amount of task-relevant signal present in our brains but also by the presence of noise, which can arise from multiple sources. Internal noise, or "trial variability," manifests as trial-by-trial variations in neural responses under seemingly identical conditions. External factors can also translate into noise, particularly when a task requires extraction of a particular type of information from our environment amid changes in other task-irrelevant "nuisance" parameters. To better understand how signal, trial variability, and nuisance variability combine to determine neural task performance, we explored their interactions, both in simulation and when applied to recorded neural data. This exploration revealed that trial variability is typically larger than a neuron's task-relevant signal for tasks with fast reaction times, where spike count integration windows are short. In this low signal-to-trial variability regime, nuisance variability has the counterintuitive property of having a negligible impact on single-neuron task performance, even when it dominates the task-relevant signal. The inconsequential impact of nuisance variability on individual neurons also extends to descriptions of population performance, under the assumption that both trial and nuisance variability are uncorrelated between neurons. These results demonstrate that some basic intuitions about neural coding are misguided in the context of a fast-processing, low-spike-count regime. NEW & NOTEWORTHY Many everyday tasks require us to extract specific information from our environment while ignoring other things. When the neurons in our brains that carry task-relevant signals are also modulated by task-irrelevant "nuisance" information, nuisance modulation is expected to act as performance-limiting noise. Using both simulated and recorded neural data, we demonstrate that these intuitions are misguided when the brain operates in a fast-processing, low-spike-count regime, where nuisance variability is largely inconsequential for performance.
任务表现不仅取决于大脑中与任务相关的信号量，还取决于噪声的存在，噪声可能来自多个来源。内部噪声，即“试验变异性”，表现为在看似相同的条件下，神经反应的逐次试验变化。外部因素也可能转化为噪声，尤其是当任务需要从环境中提取特定类型的信息时，而其他与任务无关的“干扰”参数会发生变化。为了更好地理解信号、试验变异性和干扰变异性如何组合来确定神经任务表现，我们在模拟和记录的神经数据中探索了它们的相互作用。这种探索表明，对于反应时间较快的任务，由于尖峰计数积分窗口较短，因此试验变异性通常比神经元的任务相关信号大。在这种低信号-试验变异性的情况下，干扰变异性具有反直觉的特性，即即使它主导了任务相关信号，对单个神经元的任务表现也几乎没有影响。干扰变异性对单个神经元的影响不大，这也扩展到了对群体性能的描述，假设神经元之间的试验和干扰变异性是不相关的。这些结果表明，在快速处理、低尖峰计数的情况下，一些关于神经编码的基本直觉是有误导性的。新的和值得注意的是，许多日常任务要求我们从环境中提取特定信息，同时忽略其他信息。当我们大脑中携带任务相关信号的神经元也受到与任务无关的“干扰”信息的调制时，干扰调制预计会作为限制性能的噪声。使用模拟和记录的神经数据，我们证明了当大脑在快速处理、低尖峰计数的情况下运作时，这些直觉是有误导性的，在这种情况下，干扰变异性对性能的影响在很大程度上是无关紧要的。
