Circuit Mechanisms Underlying Persistent Activity in a Neural Integrator

摘要 短期记忆功能通常通过持续的活动来支持， 神经元群体在记忆刺激偏移后的反应。这种形式的 活动是包括导航、运动控制和决策在内的各种任务的基础。经典 机械论通过模型将这种活动理想化， 只编码单一变量的同质神经元群体， 稳定的活动模式。这与最近的研究结果形成了鲜明的对比，这些研究表明，在真实的生物学中， 记忆网络表现出多个刺激属性的多路编码，随时间变化 整个人群的反应，以及上下文依赖性。在这里，我们解决电路 机制和作用，这种多样性的功能，通过一个结合的实验-理论 approach.实验在斑马鱼幼体凝视的短期记忆回路中进行 通过精确地保持眼睛在视觉上而有助于稳定视觉的控制系统 目标利用该系统的定量精度和实验易操作性， 我们将联合收割机的全回路、突触分辨率解剖与全回路记录相结合， 在细胞分辨率的活动扰动。在目标1中，我们将这些数据联合收割机组合成 神经元与实验记录的神经元以一对一的方式映射的系统。这 使我们能够推断不同解剖学，基因型， 和功能细胞类别，并形成这些相互作用如何支配电路功能的预测。 在目标2中，我们使用3D细胞分辨率光学成像和神经元的刺激扰动， 活动来完善我们的模型和测试模型预测。在目标3中，我们扩大能力， 通过开发和应用细胞类内和细胞类间相互作用的精确表征 记忆回路中神经元的3D抑制。总之，这项工作承诺大大 扩大我们对电路机制和细胞类型多样性在持续性中的作用的理解 放电、短期记忆和运动控制。

Abstract Short-term memory function is commonly supported through persistent activity, the sustained response of populations of neurons following the offset of a memorized stimulus. This form of activity underlies diverse tasks including navigation, motor control, and decision-making. Classic mechanistic theories have idealized such activity through models that assume strongly homogeneous populations of neurons that encode only a single variable and generate perfectly stable patterns of activity. This contrasts with recent work showing that neurons in real biological memory networks exhibit multiplexed encoding of multiple stimulus attributes, temporally varying responses across the population, and context dependence. Here we address the circuit mechanisms and role of this diversity in function through a combined experimental-theoretical approach. Experiments are conducted in a short-term memory circuit of the larval zebrafish gaze control system that contributes to stable vision by precisely maintaining the eyes on a visual target. Taking advantage of the quantitative precision and experimental tractability of this system, we combine whole-circuit, synapse-resolution anatomy with circuit-wide recordings and perturbations of activity at cellular resolution. In Aim 1, we combine these data into a model of the system in which neurons map in a one-to-one manner with experimentally recorded neurons. This enables us to infer the interactions within and between neurons of different anatomical, genotypic, and functional cell classes and form predictions for how these interactions govern circuit function. In Aim 2, we use 3D cellular resolution optical imaging and stimulating perturbations of neuronal activity to refine our model and test model predictions. In Aim 3, we expand our capacity to form precise characterizations of within and between cell-class interactions by developing and applying 3D suppression of neurons across the memory circuit. Altogether, this work promises to greatly expand our understanding of the circuit mechanisms and role of cell type diversity in persistent firing, short-term memory, and motor control.