Research Project 3 - Theory and computation of internal state dynamics

研究项目3 - 内态动力学理论与计算

• 批准号: 10490241
• 负责人: Surya Ganguli
• 金额: $ 101.45万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10490241
• 关键词: Algorithms; Attention; Behavior; Biological Models; Brain; Brain region; Cells; Collaborations; Computing Methodologies; Data; Dimensions; Doctor of Philosophy; Feedback; Freedom; Generations; Hippocampus (Brain); Joints; Knowledge; Learning; Macaca; Maintenance; Measures; Methods; Modeling; Monkeys; Mus; Neurons; Neurosciences; Observational Study; Optics; Outcome; Pattern; Play; Positioning Attribute; Property; Reading; Research Project Grants; Research Project Summaries; Resolution; Response to stimulus physiology; Role; Satiation; Sensory; Signal Transduction; Synapses; System; Technology; Testing; Thirst; Time; Uncertainty; Update; Visual; Work; Writing; base; cell type; clinically relevant; computational neuroscience; computer framework; computerized tools; control theory; design; excitatory neuron; experimental study; field theory; flexibility; inhibitory neuron; insight; millisecond; multisensory; network models; neural circuit; neural network; next generation; oculomotor; optogenetics; recruit; relating to nervous system; response; sensory input; sensory integration; sensory system; spatiotemporal; statistics; theories; tool; virtual reality

Research Project 3 - Theory and computation of internal state dynamics Leads: Surya Ganguli PhD and Krishna Shenoy PhD (with David Sussillo PhD) Project Summary This research project will develop both theoretical principles and computational methods for elucidating how external inputs interact with diverse internal neural state dynamics to drive fundamental neural computations, including: (1) the generation of accurate percepts through cortical amplification of weak sensory inputs amidst spontaneous background activity; (2) Bayesian integration of multisensory inputs to compute internal estimates of external state variables and their uncertainty; and (3) the triggering and maintenance of discrete internal attractor states dictating stable behaviors. Additionally, we will develop general and widely applicable computational tools to empower the simultaneous all-optical read-write multi-SLM technology, developed in RP1 and generalized to RP2 and RP4. These tools will: (1) employ state of the art systems identification methods to algorithmically extract from neural data network models of internal state dynamics; and (2) employ model based control theoretic methods to identify interesting optogenetic stimulation patterns that can both reveal computational insights into, as well as enable control of, the dynamics of cortical circuits. Our theories and computational tools for systems identification, insight and control, will both drive the design of experiments, and in-turn be iteratively refined by the outcomes of these experiments, across all RP’s. In Aim 1 we will develop theories for how the interplay of external inputs, and internal spontaneous activity in spiking neural networks with multiple cell-types, can set fundamental limits on the perceptual sensitivity of cortical networks. We will iteratively test these theories in a tight theory-experiment loop across 2 homologous sensory systems: mouse V1 in RP1 and macaque V1 in RP2. This parallel study will enable us to elucidate both convergent and divergent properties of the fundamental computation of sensory amplification in two different cortical networks that differ drastically in scale. We will also explore how the interplay between spontaneous and evoked activity is modified by diverse internal state changes, including attention, thirst, satiety, and top- down control of V1 in tight collaboration with experiments done in RP1 and RP2. In Aim 2 we will develop theories for how neural circuits with basic synaptic and cellular properties can combine internal states with external inputs to perform Bayesian integration of evidence. We will iteratively test and refine such theories in theory driven experiments on Bayesian updating of position in mouse V1, MEC, RSC and hippocampus in RP4, and Bayesian updating of evidence in joint recordings of macaque V1 and FEF in RP2. And in Aim 3 we will develop our generalized computational tools described above for systems identification, insight and control, and we will validate them by studying diverse internal state dynamics, including the robustness and flexibility of attractor transitions in mouse OFC in RP1 and mouse RSC in RP4, Bayesian integration of position in mouse RSC in RP4 and Bayesian integration of evidence in macaque V1-FEF loops in RP2. Thus overall, RP3 plays a unifying role through tight bi-directional feedback loops with RP1, RP2, and RP4 and the DSC.

研究项目3 - 内态动力学理论与计算 领导者：Surya Ganguli 博士和 Krishna Shenoy 博士（与 David Sussillo 博士） 项目概要 该研究项目将开发理论原理和计算方法，以阐明如何 外部输入与不同的内部神经状态动力学相互作用以驱动基本的神经计算， 包括：（1）通过皮层放大微弱的感觉输入来产生准确的知觉 自发的背景活动； (2) 多感官输入的贝叶斯积分计算内部 外部状态变量及其不确定性的估计； (3)离散的触发和维持 内部吸引子状态决定稳定的行为。此外，我们将开发通用且广泛适用的 计算工具支持同时全光读写多SLM技术，开发于 RP1 并推广到 RP2 和 RP4。这些工具将：（1）采用最先进的系统识别 从内部状态动力学的神经数据网络模型中通过算法提取的方法； (2) 雇用 基于模型的控制理论方法来识别有趣的光遗传学刺激模式，这些模式既可以 揭示对皮层回路动态的计算洞察并实现对其动态的控制。我们的理论 以及用于系统识别、洞察和控制的计算工具，都将推动设计 实验，然后根据这些实验的结果在所有 RP 中进行迭代完善。瞄准 1 我们将发展关于外部输入和内部自发活动如何相互作用的理论 具有多种细胞类型的神经网络可以对皮质的感知敏感性设置基本限制 网络。我们将在 2 个同源感官的紧密理论实验循环中迭代测试这些理论 系统：RP1 中的小鼠 V1 和 RP2 中的猕猴 V1。这项平行研究将使我们能够阐明 两种不同的感觉放大基本计算的收敛和发散特性 规模截然不同的皮质网络。我们还将探讨自发性之间如何相互作用 诱发的活动会受到各种内部状态变化的影响，包括注意力、口渴、饱腹感和顶部感。 与 RP1 和 RP2 中进行的实验密切合作，下调 V1 的控制。在目标 2 中，我们将开发 具有基本突触和细胞特性的神经回路如何将内部状态与 用于执行证据的贝叶斯整合的外部输入。我们将反复测试和完善这些理论 理论驱动的 RP4 小鼠 V1、MEC、RSC 和海马位置贝叶斯更新实验， RP2 中猕猴 V1 和 FEF 联合记录证据的贝叶斯更新。在目标 3 中，我们将 开发上述通用计算工具，用于系统识别、洞察和控制， 我们将通过研究不同的内部状态动态来验证它们，包括鲁棒性和灵活性 RP1 中的小鼠 OFC 和 RP4 中的小鼠 RSC 中的吸引子转变，位置的贝叶斯积分 RP4 中的小鼠 RSC 和 RP2 中猕猴 V1-FEF 环中证据的贝叶斯整合。因此总体而言，RP3 通过与 RP1、RP2、RP4 和 DSC 的紧密双向反馈回路发挥统一作用。