Department of Neurobiology, Duke University Medical Center, Durham, North Carolina 27710.
Department of Neurobiology, Duke University Medical Center, Durham, North Carolina 27710
J Neurosci. 2019 May 15;39(20):3867-3881. doi: 10.1523/JNEUROSCI.3172-18.2019. Epub 2019 Mar 4.
Sensory information is encoded by populations of cortical neurons. Yet, it is unknown how this information is used for even simple perceptual choices such as discriminating orientation. To determine the computation underlying this perceptual choice, we took advantage of the robust visual adaptation in mouse primary visual cortex (V1). We first designed a stimulus paradigm in which we could vary the degree of neuronal adaptation measured in V1 during an orientation discrimination task. We then determined how adaptation affects task performance for mice of both sexes and tested which neuronal computations are most consistent with the behavioral results given the adapted population responses in V1. Despite increasing the reliability of the population representation of orientation among neurons, and improving the ability of a variety of optimal decoders to discriminate target from distractor orientations, adaptation increases animals' behavioral thresholds. Decoding the animals' choice from neuronal activity revealed that this unexpected effect on behavior could be explained by an overreliance of the perceptual choice circuit on target preferring neurons and a failure to appropriately discount the activity of neurons that prefer the distractor. Consistent with this all-positive computation, we find that animals' task performance is susceptible to subtle perturbations of distractor orientation and optogenetic suppression of neuronal activity in V1. This suggests that to solve this task the circuit has adopted a suboptimal and task-specific computation that discards important task-related information. A major goal in systems neuroscience is to understand how sensory signals are used to guide behavior. This requires determining what information in sensory cortical areas is used, and how it is combined, by downstream perceptual choice circuits. Here we demonstrate that when performing a go/no-go orientation discrimination task, mice suboptimally integrate signals from orientation tuned visual cortical neurons. While they appropriately positively weight target-preferring neurons, they fail to negatively weight distractor-preferring neurons. We propose that this all-positive computation may be adopted because of its simple learning rules and faster processing, and may be a common approach to perceptual decision-making when task conditions allow.
感觉信息由皮质神经元群体编码。然而，人们尚不清楚这种信息如何用于进行简单的感知选择，例如辨别方向。为了确定这种感知选择的计算基础，我们利用了小鼠初级视觉皮层（V1）中强大的视觉适应。我们首先设计了一种刺激范式，在此范式中，我们可以在进行方向辨别任务时改变 V1 中神经元适应的程度。然后，我们确定了适应如何影响雌雄小鼠的任务表现，并测试了哪种神经元计算最符合 V1 中适应群体反应的行为结果。尽管增加了神经元群体对方向的代表性的可靠性，并且提高了各种最优解码器辨别目标与干扰物方向的能力，但适应会增加动物的行为阈值。从神经元活动中解码动物的选择表明，这种对行为的意外影响可以用感知选择回路过度依赖于目标偏向神经元而无法适当降低偏向干扰物的神经元活动来解释。与这种全正计算一致，我们发现动物的任务表现容易受到干扰物方向的微妙干扰和 V1 中神经元光遗传学抑制的影响。这表明，为了完成这项任务，该回路采用了一种次优的、特定于任务的计算方法，丢弃了重要的与任务相关的信息。系统神经科学的主要目标之一是了解感觉信号如何用于指导行为。这需要确定在下游感知选择回路中，感觉皮质区域中的哪些信息被使用，以及如何被组合。在这里，我们证明在执行 Go/No-Go 方向辨别任务时，小鼠不能最优地整合来自方向调谐视觉皮层神经元的信号。虽然它们适当的正向加权目标偏向神经元，但它们未能负向加权干扰物偏向神经元。我们提出，这种全正计算可能是由于其简单的学习规则和更快的处理而被采用的，并且当任务条件允许时，它可能是一种常见的感知决策方法。
