CRCNS: The neural basis of probabilistic inference in the visual system

将传入的感官信息与先前学习的知识相结合是 感觉皮层的计算，但它仍然知之甚少。为了研究神经基础， 这个在视觉皮层的计算我们结合了联合收割机三个关键技术。首先，我们采用严格的 数学框架来预测感觉神经元的活动应该如何随着 学习并依赖于任务。第二，我们通过消除那些 假设输入携带先前学习的信息。第三，我们记录下 许多初级视觉皮层（V1）神经元同时活动，有和没有这些输入。 我们将在三个重要场景中结合使用这三种联合收割机。在第一种情况下，我们将测量 并分析V1反应如何在学习两个不同版本的 方向辨别任务。我们将使用这些新数据来验证我们的理论框架， 替代理论。在第二种情况下，我们将分析V1的反应，而受试者是 多任务处理，在两个不同的任务之间切换。这将提供对性能来源的洞察 由于多任务的限制，并进入分层决策的基础。在第三种情况下，我们 “{将继续执行V1活动以进行顺序删除”。正在执行任务！在大脑超重的情况下， （显示“确认偏差”），以及何时不显示。这将使我们能够测试一个新的计算 在视觉领域的确认偏见的帐户。 我们的研究结果将解决系统神经科学中的几个重要争论： 与感觉区域的反馈联系感觉神经元相关变异性的来源和作用是什么？ 回应？哪种数学框架最能描述感官计算？ 相关性（参见说明）： 这个项目将研究大脑如何将视网膜上的视觉信息与先验知识结合起来 为了支持视觉感知和决策。深入了解神经系统 这些过程的基础将帮助我们了解精神分裂症，自闭症等疾病的影响， ADHD对视觉处理和功能的影响。

Combining incoming sensory information with previously learned knowledge is one of the fundamental computations of sensory cortex, yet It remains poorly understood. In order to investigate the neural basis of this computation in visual cortex we combine three key techniques. First, we employ a rigorous mathematical framework to make predictions about how the activity of sensory neurons should change with learning and depend on the task. Second, we causally manipulate the neural circuitry by eliminating those inputs that have been hypothesized to carry previously learned information. Third, we record the spiking activity of many primary visual cortex (V1) neurons simultaneously, with and without those inputs. We will combine these three techniques in three important scenarios. In the first scenario, we will measure and analyze how V1 responses change over the course of learning two different versions of an orientation-discrimination task. We will use this new data to validate our theoretical framework and compare it to alternative theories. In the second scenario, we will analyze V1 responses while the subject is multitasking, switching between two different tasks. This will provide insights into the source of performance limitations due to multitasking and into the basis of hierarchical decision-making. In the third scenario, we \'{ill c_on1pare V1 activity for sequential de~ision'.rnaking taskswh!Jn the brain over-weig_h_ts e§rly E)Vi_dencE) (displaying a 'confirmation bias'), and when it does not. This will allow us to test a new computational account of the confirmation bias in the visual domain. Our results will address several important debates in systems neuroscience: What is the function of feedback connections to sensory areas? What is the source and role of correlated variability of sensory responses? What mathematical framework best describes sensory computations? RELEVANCE (See instructions): This project will study how the brain combines the visual information on the retina with prior knowledge about the world, in order to support visual perception and decision-making. Insights into the neurological basis of those processes will help us understand the effect of diseases such as schizophrenia, autism, and ADHD on visual processing and function.