Discovering dynamic computations from large-scale neural activity recordings

从大规模神经活动记录中发现动态计算

• 批准号: 9789277
• 负责人: Tatiana Engel
• 金额: $ 44.16万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789277
• 关键词: Address; Algorithms; Amines; Area; Behavior; Behavioral; Brain; Brain region; Complex; Computer Simulation; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Decision Making; Dimensions; Dissection; Elements; Epilepsy; Heterogeneity; Hour; Individual; Link; Maps; Mental disorders; Methods; Modeling; Mus; Neurons; Neurosciences; Parietal; Parietal Lobe; Parkinson Disease; Pattern; Perception; Plant Roots; Population; Population Dynamics; Primates; Research; Resolution; Schizophrenia; Series; Speed; Stable Populations; Techniques; Technology; Testing; Theoretical model; Time; Uncertainty; Visualization software; Work; cell type; cognitive function; computer framework; computerized data processing; data modeling; design; driving behavior; driving force; dynamic system; inhibitory neuron; mathematical algorithm; millisecond; neural circuit; neural patterning; novel; optical imaging; reconstruction; relating to nervous system; repository; response; systems of equations; theories

Project Summary/Abstract How neural activity is coordinated within local microcircuits and across brain regions to drive behavior is a central open question in neuroscience. Recent advances in massively-parallel neural recording tech- nologies are producing dynamic activity maps during complex behaviors, with single-neuron granularity and single-spike resolution. To reveal fundamental dynamic features in these large-scale datasets, new principled and scalable computational methods are urgently needed. To address this need, we will de- velop a broadly applicable, non-parametric inference framework for discovering dynamic computations from large-scale neural activity recordings. Our framework seeks a dynamical model of the data, but unlike existing techniques, does not require a priori model assumptions. Existing techniques commonly fit data with simple ad hoc models, which often miss or distort defining dynamic features. Instead, our non-parametric approach explores the entire space of all possible dynamics in search for the model consistent with the data, and thereby eliminates a priori guess work, ambiguous model comparisons and model-induced biases. We aim to develop optimization algorithms to effectively search through the space of all dynamical models, implement these algorithms on GPUs to achieve maximal computational speed, and derive information-theoretic bounds to quantify reliability of our computational methods. To demonstrate how our novel methods aid scientific discovery, we will employ them to examine decision- related activity in parietal and premotor cortices. While different theoretical models of decision-making have been proposed, it still remains unknown how decision computations are implemented on the level of individual neurons and neural populations. Our analyses will offer the first computational models of decision-making rooted directly in neural data, reconcile stability of population dynamics with hetero- geneity of single-neuron responses, reveal differences in decision-computations across cortical layers, and identify differences in decision-related dynamics of excitatory vs. inhibitory neurons.

项目总结/摘要 神经活动是如何在局部微电路和大脑区域之间协调以驱动行为的， 神经科学中的一个核心问题并行神经记录技术的最新进展 神经元在复杂行为期间以单神经元粒度生成动态活动图 和单峰值分辨率。为了揭示这些大规模数据集中的基本动态特征， 迫切需要有原则的和可扩展的计算方法。为了满足这一需求，我们将... velop一个广泛适用的，非参数推理框架，用于发现动态计算 从大规模的神经活动记录。我们的框架寻求数据的动态模型，但 与现有技术不同，不需要先验模型假设。现有技术通常 用简单的特设模型拟合数据，这些模型往往会错过或扭曲定义动态特征。而我们 非参数方法探索所有可能的动力学的整个空间，以寻找模型 与数据一致，从而消除了先验猜测工作，模糊的模型比较 和模型诱导的偏差。我们的目标是开发优化算法，以有效地搜索 所有动态模型的空间，在GPU上实现这些算法，以实现最大的计算 速度，并推导出信息理论界来量化我们的计算方法的可靠性。到 展示我们的新方法如何帮助科学发现，我们将利用它们来检查决策， 顶叶和前运动皮层的相关活动。虽然不同的决策理论模型 虽然已经提出了，但仍然不知道如何在层次上实现决策计算 单个神经元和神经群体的。我们的分析将提供第一个计算模型， 决策直接植根于神经数据，调和人口动态的稳定性与异质性， 对单个神经元反应的分析，揭示了大脑皮层决策计算的差异， 并确定兴奋性与抑制性神经元的决策相关动力学差异。