A cell-type specific explanation of visual decision circuits.

视觉决策电路的细胞类型特定解释。

• 批准号: 10735026
• 负责人: ANNE KATHRYN CHURCHLAND
• 金额: $ 38.68万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735026
• 关键词: Address; Affect; Anatomy; Animals; Anterolateral; Area; Auditory; Award; Behavior; Brain; Calcium; Cells; Cognitive; Communication; Corpus striatum structure; Coupled; Coupling; Data; Decision Making; Development; Disease; Dorsal; Event; Goals; Image; Individual; Interneurons; Laboratories; Life; Linear Models; Link; Maps; Measurement; Measures; Modeling; Molecular; Motor; Movement; Neurons; Pathway interactions; Pattern; Performance; Pontine structure; Population; Positioning Attribute; Pyramidal Tracts; Reporting; Retina; Role; Sensory; Shapes; Short-Term Memory; Signal Transduction; Specific qualifier value; Specificity; Spinal Cord; Stimulus; Stress Tests; Structure; Testing; Thalamic structure; Theoretical model; Time; Training; Update; Visual; Work; area striata; candidate identification; cell type; clinically relevant; excitatory neuron; experimental study; extrastriate visual cortex; hippocampal pyramidal neuron; inhibitory neuron; insight; mature animal; neural; neural circuit; neuromechanism; novel; optogenetics; recruit; response; superior colliculus Corpora quadrigemina; transcription factor; two-photon; visual stimulus

Abstract The main goal of my laboratory is to understand the neural circuits that support perceptual decision-making. Recent efforts to understand microcircuits within decision-making areas have been fruitful, thanks in part to the ability to identify and manipulate distinct types of inhibitory neurons. Our understanding how distinct types of excitatory pyramidal neurons (PyNs) shape circuits has lagged behind. This lack of understanding is particularly problematic for decision circuits in which PyN type can determine the long-range project target of neurons within an area. To surmount this problem, we propose to compare the contribution of distinct PyN types to key decision- making computations: working memory and evidence accumulation. We will measure and manipulate neural activity of intratelencephalic (IT) and pyramidal tract (PT) neurons in animals trained to make decisions about the stochastically-varying spatial position of visual gratings. We seek to understand how PT and IT neurons in multiple neural structures collectively support the computations needed for decision-making. In Aim 1, we will use widefield calcium imaging to generate cortex-wide activity maps for each PyN type. We will compare the spatial and temporal patterns at multiple scales, and will deploy quantitative analyses to identify candidate regions for decision-making computations. In Aim 2, we will measure the activity of single cells within these regions using 2-photon imaging. We will use a generalized linear model (GLM) to estimate the extent to which single neurons are modulated by relevant variables, such as stimulus events, choice, and movements. We will compare these across PT and IT neurons. The same models will estimate the magnitude and time course of “coupling” between neurons, which we will compare across PyN types and regions. Shorter coupling time courses are expected for neurons that support sensory encoding, while longer coupling time courses are expected for neurons that support working memory. We will use these observations to generate a new, biologically realistic model that includes PyN types in multiple areas working together to support decision-making computations. In Aim 3, we will evaluate a causal role for PyN types using cell-type specific optogenetic inactivation. We will compare multiple aspects of decision-making behavior on trials with vs. without inactivation. For example, we will determine how inactivation affects decision accuracy, and the extent to which this depends on when inactivation occurs. We will also determine how inactivation impacts the animal’s ability to accumulate sensory evidence and/or drives movements that can be detected with a classifier trained on video data. We will use this data to “stress test” the model, evaluating whether inactivations in our artificial network generate the same changes as inactivations in the real brain. If not, we will update and re-test the model, creating a tight loop between experiments and modeling. Taken together, the proposed work will advance our understanding of decision-making beyond the non-specific approach that has limited a mechanistic understanding thus far.

摘要 我实验室的主要目标是了解支持感知决策的神经回路。 最近在理解决策领域内的微电路方面的努力取得了丰硕成果，这部分归功于 识别和操纵不同类型的抑制性神经元的能力。我们了解到不同类型的 兴奋性锥体神经元（PyN）形状电路已经落后。这种不理解尤其 这对于其中PyN类型可以确定内部神经元的远程项目目标的决策电路是有问题的。 一个区域。为了克服这个问题，我们建议比较不同的PyN类型对关键决策的贡献， 进行计算：工作记忆和证据积累。我们将测量和操纵神经 在接受训练的动物中，端脑内（IT）和锥体束（PT）神经元的活动， 视觉光栅的随机变化的空间位置。我们试图了解PT和IT神经元如何在 多个神经结构共同支持决策所需的计算。在目标1中，我们 使用宽视野钙成像来生成每种PyN类型的皮层范围活动图。我们将比较 并将部署定量分析， 用于决策计算的区域。在目标2中，我们将测量这些细胞中单细胞的活性。 使用双光子成像的区域。我们将使用广义线性模型（GLM）来估计 单个神经元受到相关变量的调节，例如刺激事件、选择和运动。我们将 比较PT和IT神经元之间的差异。同样的模型将估计 神经元之间的“耦合”，我们将在PyN类型和区域之间进行比较。更短的耦合时间 支持感觉编码的神经元预计会有更长的耦合时间， 支持工作记忆的神经元。我们将利用这些观察结果来生成一个新的， 一个生物现实模型，包括多个领域的PyN类型，共同支持决策制定 计算。在目标3中，我们将使用细胞类型特异性光遗传学方法评估PyN类型的因果作用。 失活我们将比较有与无失活试验的决策行为的多个方面。 例如，我们将确定失活如何影响决策准确性，以及这在多大程度上取决于 当失活发生时打开。我们还将确定失活如何影响动物的积累能力， 感官证据和/或驱动可以用在视频数据上训练的分类器检测的运动。我们将 使用这些数据对模型进行“压力测试”，评估我们的人工网络中的失活是否会产生 与真实的大脑中的失活相同的变化。如果没有，我们将更新并重新测试模型，创建一个紧密的循环 在实验和建模之间。总的来说，拟议的工作将促进我们对以下问题的理解： 决策超越了非特定的方法，限制了机械的理解到目前为止。

项目成果

Perceptual Decision-Making: A Field in the Midst of a Transformation.

• DOI: 10.1016/j.neuron.2018.10.017
• 发表时间: 2018-10-24
• 期刊: Neuron
• 影响因子: 16.2
• 作者: Najafi F;Churchland AK
• 通讯作者: Churchland AK

Decision-making behaviors: weighing ethology, complexity, and sensorimotor compatibility.

• DOI: 10.1016/j.conb.2017.11.001
• 发表时间: 2018-04
• 期刊: Current opinion in neurobiology
• 影响因子: 5.7
• 作者: Juavinett AL;Erlich JC;Churchland AK
• 通讯作者: Churchland AK

Lapses in perceptual decisions reflect exploration.

• DOI: 10.7554/elife.55490
• 发表时间: 2021-01-11
• 期刊: eLife
• 影响因子: 7.7
• 作者: Pisupati S;Chartarifsky-Lynn L;Khanal A;Churchland AK
• 通讯作者: Churchland AK

Pyramidal cell types drive functionally distinct cortical activity patterns during decision-making.

• DOI: 10.1038/s41593-022-01245-9
• 发表时间: 2023-03
• 期刊: NATURE NEUROSCIENCE
• 影响因子: 25
• 作者: Musall, Simon;Sun, Xiaonan R. R.;Mohan, Hemanth;An, Xu;Gluf, Steven;Li, Shu-Jing;Drewes, Rhonda;Cravo, Emma;Lenzi, Irene;Yin, Chaoqun;Kampa, Bjoern M.;Churchland, Anne K. K.
• 通讯作者: Churchland, Anne K. K.