Determining how neural coding and readout depend on internal state and past experience

确定神经编码和读出如何依赖于内部状态和过去的经验

• 批准号: 9983226
• 负责人: Dietmar Plenz
• 金额: $ 61.61万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9983226
• 关键词: Acute; Address; Affect; Afferent Neurons; Animals; Area; Arousal; Auditory area; Behavior; Behavioral; Brain; Code; Ensure; Eye Movements; Hour; Individual; Learning; Life; Locomotion; Maintenance; Measures; Methods; Neurons; Perception; Perceptual learning; Performance; Population; Population Statistics; Process; Resolution; Response to stimulus physiology; Role; Sensory; Signal Transduction; Stimulus; Testing; Time; Weights and Measures; area striata; behavior influence; behavioral outcome; experience; experimental study; flexibility; improved; individual response; information processing; insight; millisecond; neural network; neuronal circuitry; novel; olfactory bulb; relating to nervous system; response; sensory cortex; spatiotemporal; statistics; stimulus processing; two-photon

Sensory representations in the brain and an animal’s perception change in many ways over many time scales. Over tens or hundreds of milliseconds, ongoing neuronal activity contributes substantially to response variability in primary sensory areas. Effort and arousal typically vary over seconds or minutes, and are also associated with major changes in sensory representations and behavioral performance. Perceptual learning occurs over hours and days, imparting new perceptual capabilities. Working with all these forms of variability in stimulus processing, the brain maintains (in the case of ongoing activity) and improves (in the case of arousal and learning) behavioral outcomes. It is commonly assumed that maintenance and improvements in behavioral outcome depend primarily on changes in the corresponding sensory representation, yet this is far from certain. New methods of two-photon stimulation are ideal for probing how much the contributions of different cortical neurons change across behavioral states or as animals learn new perceptual tasks. The proposed experiments take advantage of the experimental accessibility of stimulus-response associations in primary sensory cortices to identify mechanisms and principles in neuronal circuits that maintain and improve behavioral outcome in the context of brain state changes over many timescales. These studies will test whether the high spatiotemporal variability of ongoing activity reflects higher-order statistics of neuronal population activity that ensure the most informative stimulus processing and best behavioral outcomes. The impact of effort and arousal will be addressed at cellular resolution by identifying changes in population representations and readout in primary sensory cortex between different behavioral states and during saccadic suppression. Perceptual learning experiments will probe the contributions to behavioral performance of individual neurons in the olfactory bulb, primary auditory cortex, and primary visual cortex, and determine how those contributions change and can be manipulated over the course of perceptual learning. Collectively, these experiments will provide a far more precise and granular view of how sensory representations vary over different time scales, and new information on how the decoding of those representations can change over time.

大脑中的感觉表征和动物的感知在许多时间尺度上以多种方式发生变化。 在数十或数百毫秒内，持续的神经元活动对反应有很大的贡献 主要感觉区的可变性。努力和唤醒通常在几秒钟或几分钟内有所不同，而且 与感觉表征和行为表现的重大变化有关。知觉学习 在几个小时和几天内发生，赋予新的感知能力。在中使用所有这些形式的可变性 刺激处理，大脑维持(在持续活动的情况下)和改善(在唤醒的情况下 和学习)行为结果。 通常认为，行为结果的维持和改善主要取决于 相应的感觉表征发生了变化，但这还远未确定。双光子的新方法 刺激是探索不同皮质神经元的贡献变化程度的理想方法 行为状态或动物学习新的知觉任务。拟议中的实验利用了 初级感觉皮层刺激-反应联系识别的实验可达性 神经元回路中维持和改善行为结果的机制和原理 大脑状态在许多时间尺度上都会发生变化。这些研究将测试是否存在高度的时空变异性 正在进行的活动反映了神经元群体活动的高阶统计量，确保了信息最丰富 刺激处理和最佳行为结果。努力和唤醒的影响将在 通过识别初级感觉皮质中群体表征和读数的变化来实现细胞分辨率 在不同的行为状态之间和在跳动抑制期间。知觉学习实验将 探讨嗅球、初级听觉单个神经元对行为表现的贡献 大脑皮层和初级视觉皮质，并确定这些贡献是如何变化的，以及如何被操纵 知觉学习的过程。 总的来说，这些实验将提供一个更精确和更细粒度的感官观察 表示在不同的时间尺度上有所不同，以及关于如何解码这些的新信息 表达可能会随着时间的推移而改变。