Research Project 3 - Theory and computation of internal state dynamics

研究项目3--内态动力学的理论与计算 主演：苏里亚·甘古利博士和克里希纳·谢诺伊博士(大卫·苏西洛博士) 项目摘要 这项研究项目将发展理论原理和计算方法来阐明 外部输入与不同的内部神经状态动态相互作用以驱动基本神经计算， 包括：(1)通过皮层放大弱感觉输入而产生准确的知觉 自发背景活动；(2)多感官输入的贝叶斯集成计算内部 外部状态变量及其不确定性的估计；以及(3)离散状态变量的触发和维护 内部吸引子状态决定了稳定的行为。此外，我们还将开发通用的和广泛适用的 支持同步全光读写多SLM技术的计算工具，开发于 Rp1，并推广到Rp2和Rp4。这些工具将：(1)使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进行算法提取的方法；以及(2)使用 基于模型的控制理论方法来识别有趣的光遗传刺激模式，这些模式可以 揭示对大脑皮层回路动力学的计算洞察力，并使其能够控制。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 实验，并反过来通过这些实验的结果迭代地精炼所有的RP。 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于尖峰的理论。 具有多种细胞类型的神经网络可以设置大脑皮层知觉敏感度的基本限制 网络。我们将在一个紧密的理论-实验循环中，通过两个同源感官反复测试这些理论 系统：RP1为小鼠V1，RP2为猕猴V1。这项平行研究将使我们能够阐明 两种不同类型感觉放大基本计算的收敛和发散性质 在规模上大不相同的大脑皮层网络。我们还将探索自发之间的相互作用 而诱发的活动会被不同的内部状态变化所改变，包括注意力、口渴、饱腹感和精神紧张。 与在RP1和RP2中进行的实验密切合作，向下控制V1。在目标2中，我们将开发 具有基本突触和细胞特性的神经电路如何将内部状态与 外部输入以执行证据的贝叶斯集成。我们将反复测试和提炼这些理论 RP4小鼠V1、MEC、RSC和海马区位置贝叶斯更新的理论驱动实验 以及RP2中猕猴V1和FEF联合记录中证据的贝叶斯更新。在《目标3》中，我们将 开发上述用于系统识别、洞察和控制的通用计算工具， 我们将通过研究不同的内部状态动力学来验证它们，包括健壮性和灵活性 Rp1中的小鼠OFC和Rp4中的小鼠RSC的吸引子转换，位置的贝叶斯积分 RP4中的小鼠RSC和RP2中猕猴V1-FEF循环中的贝叶斯证据整合。因此，总的来说，RP3 通过与RP1、RP2、RP4和DSC的紧密双向反馈环路发挥统一作用。

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.