Brain and Cognitive Sciences, Center for Visual Science, University of Rochester, Rochester, New York, United States.
Neurobiology, Harvard Medical School, Boston, Massachusetts, United States.
J Neurophysiol. 2023 May 1;129(5):1021-1044. doi: 10.1152/jn.00085.2022. Epub 2023 Mar 22.
A central goal of systems neuroscience is to understand how populations of sensory neurons encode and relay information to the rest of the brain. Three key quantities of interest are ) how mean neural activity depends on the stimulus (sensitivity), ) how neural activity (co)varies around the mean (noise correlations), and ) how predictive these variations are of the subject's behavior (choice probability). Previous empirical work suggests that both choice probability and noise correlations are affected by task training, with decision-related information fed back to sensory areas and aligned to neural sensitivity on a task-by-task basis. We used Utah arrays to record activity from populations of primary visual cortex (V1) neurons from two macaque monkeys that were trained to switch between two coarse orientation-discrimination tasks. Surprisingly, we find no evidence for significant trial-by-trial changes in noise covariance between tasks, nor do we find a consistent relationship between neural sensitivity and choice probability, despite recording from well-tuned task-sensitive neurons, many of which were histologically confirmed to be in supragranular V1, and despite behavioral evidence that the monkeys switched their strategy between tasks. Thus our data at best provide weak support for the hypothesis that trial-by-trial task-switching induces changes to noise correlations and choice probabilities in V1. However, our data agree with a recent finding of a single "choice axis" across tasks. They also raise the intriguing possibility that choice-related signals in early sensory areas are less indicative of task learning per se and instead reflect perceptual learning that occurs in highly overtrained subjects. Converging evidence suggests that decision processes affect sensory neural activity, and this has informed numerous theories of neural processing. We set out to replicate and extend previous results on decision-related information and noise correlations in V1 of macaque monkeys. However, in our data, we find little evidence for a number of expected effects. Our null results therefore call attention to differences in task training, stimulus design, recording, and analysis techniques between our and prior studies.
系统神经科学的一个核心目标是了解感觉神经元群体如何对刺激进行编码并将信息传递到大脑的其他部分。三个关键的关注点是:1)平均神经活动如何依赖于刺激(敏感性);2)神经活动如何围绕平均值(噪声相关性)变化;3)这些变化如何预测主体的行为(选择概率)。先前的实证研究表明,选择概率和噪声相关性都受到任务训练的影响,与决策相关的信息被反馈到感觉区域,并根据任务逐个任务进行调整,以与神经敏感性对齐。我们使用犹他电极阵列记录了两只猕猴初级视觉皮层(V1)神经元群体的活动,这些猕猴接受过训练,可在两个粗略的方向辨别任务之间切换。令人惊讶的是,我们没有发现任务之间噪声协方差在试验间有显著变化的证据,也没有发现神经敏感性和选择概率之间存在一致关系的证据,尽管我们记录了来自经过良好调谐的任务敏感神经元的活动,其中许多神经元在组织学上被证实位于 V1 的超颗粒层,并且有行为证据表明猴子在任务之间切换了策略。因此,我们的数据最多只能为试验间任务切换引起 V1 中的噪声相关性和选择概率变化的假设提供微弱的支持。然而,我们的数据与最近关于任务间存在单一“选择轴”的发现一致。它们还提出了一个有趣的可能性,即早期感觉区域中的与选择相关的信号本身并不能很好地反映任务学习,而是反映了在高度过度训练的受试者中发生的感知学习。越来越多的证据表明,决策过程会影响感觉神经活动,这为许多神经处理理论提供了信息。我们着手复制和扩展以前在猕猴 V1 中关于决策相关信息和噪声相关性的结果。然而,在我们的数据中,我们几乎没有发现许多预期效应的证据。因此,我们的零结果引起了对我们和之前研究之间任务训练、刺激设计、记录和分析技术差异的关注。