Optical measurement of causal functional connectivity in posterior parietal cortex

项目摘要 小鼠后顶叶皮质(PPC)已成为决策过程中的重要区域。 记忆引导的决策任务。PPC中的神经元通常在单个时间点选择性地反应 一种独特的行为试验类型；群体水平的活动可以被视为特定于选择的轨迹 通过状态空间。许多神经元对行为选择做出选择性反应；这些反应类似于 在包含赢家通吃动态的模型中观察到，但购买力平价作为赢家发挥作用的程度- 全食网络仍不得而知。赢家通吃神经网络的主要特征包括特定于选择的 具有不同选择偏好的神经元池之间的兴奋性连接和相互抑制。 为了在决策领域测试神经电路体系结构的模型，我们建议开发一种高速、高速的 光学测量因果函数连通性的双光子光刺激方法 神经元。我们将首先开发和表征这种通过体内细胞附着的光刺激方法 录音。接下来，我们将应用这种光刺激方法来测量 PPC中的兴奋性神经元，以测试兴奋性网络的功能特异性程度。这些实验 将单独测量的活动-行为关系与因果功能连通性测量相关联 并测试表现出相似选择偏好的神经元是否优先兴奋 彼此之间。最后，我们将测量刺激PPC区GABA能神经元的效果 执行记忆引导的两种选择强制选择任务，以测量 GABA能神经元和兴奋性神经元。如果购买力平价表现出赢家通吃的动态，我们预计会发现 GABA能神经元优先抑制具有相反选择偏好的兴奋性神经元。作为一个整体， 我们预测，这项技术将产生强大的数据，为我们提供对微电路的新见解 购买力平价中的决策。

Project Summary The mouse posterior parietal cortex (PPC) has emerged as an essential region for decision-making during memory-guided decision-making tasks. Neurons in the PPC typically respond selectively at a single point during a unique behavioral trial type; activity at the population level can be thought of as a choice-specific trajectory through state space. Many neurons respond selectively for behavioral choice; these responses resemble those observed in models incorporating winner-take-all dynamics, but the extent to which PPC functions as a winner- take-all network remains unknown. Key features of winner-take-all neural networks include choice-specific excitatory connectivity and mutual inhibition between pools of neurons with different choice preferences. To test models of neural circuit architecture in decision-making areas, we propose to develop a high-speed, high- throughput two-photon photostimulation approach to optically measure causal functional connectivity between neurons. We will first develop and characterize this photostimulation approach with in vivo cell-attached recordings. Next, we will apply this photostimulation approach to measure causal functional connectivity between excitatory neurons in PPC to test the degree of functional specificity in excitatory networks. These experiments will relate separately measured activity-behavior relationships to causal functional connectivity measurements between excitatory neurons and test whether neurons exhibiting similar choice preferences preferentially excite one another. Finally, we will measure the effect of stimulating GABAergic neurons in PPC while the mouse performs a memory-guided two-alternative forced choice task to measure causal functional connectivity between GABAergic neurons and excitatory neurons. If PPC exhibits winner-take-all dynamics, we would expect to find that GABAergic neurons preferentially inhibit excitatory neurons with opposite choice preferences. As a whole, we predict that this technique will generate powerful data that provide new insight into the microcircuitry of decision-making in PPC.