Topological bridges between circuits, models, and behavior

项目摘要 神经科学家试图用线性分析方法来理解大脑的困境类似于研究 用金属探测器找到的碎片来分析海洋的组成。我们所知道的关于神经系统的一切 决策的基础，从生物学到计算，再到行为，清楚地表明， 神经元和行为是非常非线性的。然而，由于良好的数学原因，我们试图 要理解这种关系通常只依赖于线性度量。这些措施有一个特别困难的 时间处理神经网络远非静态的现实。事实上， 神经元，虽然增加了额外的非线性，是一个至关重要的线索，潜在的机制， 计算。拟议项目的目标是检验假设， 神经元群体的可变性将1）在神经元和感知决策之间建立强有力的联系，2） 认知影响感知的回路机制的约束模型，以及3）预测 因果操纵不同亚型的抑制性中间神经元对群体活动的影响。我们将使用和 从代数拓扑学发展方法来表征神经元群体的整体活动， 非线性方式我们的项目利用了三种高度互动的方法之间的互补优势： 灵长类神经生理学和心理物理学，神经元电路建模，双光子成像， 小鼠中抑制性中间神经元亚型的光遗传学操作。在目标1中，我们将检验以下假设： 感觉和认知过程，包括对比，适应，注意力和动机影响性能 视觉任务，正是当他们改变了相关的视觉或顶叶变异的拓扑签名， 皮层在目标2中，我们将使用一个生物病理学现实模型来了解皮质回路中的哪些变化 是否会改变神经元群体活动的拓扑特征。在目标3中，我们将测试 我们的模型的预测，以了解如何操纵不同亚型的抑制活性， interneurons影响网络中神经元活动和信息处理的拓扑总结。这 这项工作使用了新颖的数学思想来连接皮层回路研究的不同层次。它将有 对我们理解神经元回路和跨物种行为之间的关系的影响， 系统和理论方法。

Project summary The plight of the neuroscientist trying to understand the brain using linear analysis methods is akin to studying the makeup of the ocean using the bits you find with a metal detector. Everything we know about the neural basis of decision making, from biology to computation to behavior, makes it clear that the relationship between neurons and behavior is profoundly nonlinear. However, for good mathematical reasons, our attempts to understand that relationship typically rely only on linear measures. These measures have an especially hard time dealing with the reality that neural networks are far from static. Indeed, the flexibility of interactions between neurons, while adding an additional nonlinearity, is a critically important clue about underlying mechanisms and computations. The goal of the proposed project is to test the hypothesis that nonlinear measures of correlated variability in a population of neurons will 1) establish a strong link between neurons and perceptual decisions, 2) constrain models of the circuit mechanisms by which cognition affects perception, and 3) predict the effects of causally manipulating different subtypes of inhibitory interneurons on population activity. We will use and develop methods from algebraic topology to characterize the activity of neuronal populations in a holistic, nonlinear way. Our project leverages the complementary strengths between three highly interactive approaches: primate neurophysiology and psychophysics, modeling neuronal circuits, and two photon imaging and optogenetic manipulation of subtypes of inhibitory interneurons in mice. In Aim 1, we will test the hypothesis that sensory and cognitive processes including contrast, adaptation, attention, and motivation affect performance on visual tasks exactly when they change the topological signatures of the correlated variability in visual or parietal cortex. In Aim 2, we will use a biophysically realistic model to understand which changes in a cortical circuit would or would not change the topological signatures of neuronal population activity. In Aim 3, we will test the predictions of our model to understand how manipulating the activity of different subtypes of inhibitory interneurons affects topological summaries of neuronal activity and information processing in the network. This work uses novel mathematical ideas to bridge different levels of the study of cortical circuits. It will have implications for our understanding of the relationship between neuronal circuits and behavior across species, systems, and theoretical approaches.