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: 10002240
• 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 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002240
• 关键词: Address; Algorithms; Amines; Area; Behavior; Behavioral; Brain; Brain region; Complex; Computer Models; Computer software; Computing Methodologies; Data; Data Analyses; 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; large scale data; 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.

项目概要/摘要 神经活动如何在局部微电路内和跨大脑区域协调以驱动行为 神经科学中的一个核心开放问题。大规模并行神经记录技术的最新进展 nologies 在复杂行为期间生成具有单神经元粒度的动态活动图 和单尖峰分辨率。为了揭示这些大规模数据集中的基本动态特征，新的 迫切需要有原则且可扩展的计算方法。为了满足这一需求，我们将 开发一个广泛适用的非参数推理框架来发现动态计算 来自大规模神经活动记录。我们的框架寻求数据的动态模型，但是 与现有技术不同，不需要先验模型假设。现有技术普遍 使用简单的临时模型来拟合数据，这通常会错过或扭曲定义的动态特征。相反，我们的 非参数方法探索所有可能动态的整个空间来寻找模型 与数据一致，从而消除先验猜测工作、模糊的模型比较 和模型引起的偏差。我们的目标是开发优化算法来有效地搜索 所有动态模型的空间，在 GPU 上实现这些算法以实现最大计算量 速度，并得出信息论界限来量化我们计算方法的可靠性。到 展示我们的新方法如何帮助科学发现，我们将用它们来检查决策 顶叶和前运动皮质的相关活动。虽然不同的决策理论模型 已经被提出，但仍然未知如何在该级别上实现决策计算 单个神经元和神经群体。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，协调种群动态的稳定性与异质性 单神经元反应的遗传性，揭示皮层各层决策计算的差异， 并确定兴奋性神经元与抑制性神经元的决策相关动态的差异。