Real-time mapping and adaptive testing for neural population hypotheses

神经群体假设的实时映射和自适应测试

基本信息

批准号：
10838394
负责人：
John Pearson
金额：
$ 20.11万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-15 至 2025-09-14
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10838394
关键词：
Algorithms Animals Behavior Behavioral Benchmarking Biological Models Brain Calcium Case Study Cells Complex Computer software Data Data Analyses Data Set Dimensions Documentation Educational workshop Electrophysiology (science)Ensure Experimental Designs Explosion Frequencies Goals ImProv Image Individual Intervention Link Maps Methods Modeling Neurons Neurosciences Nonlinear Dynamics Population Population Dynamics Process Property Recurrence Research Personnel Running Sampling Series Signal Transduction Software Tools Sorting Specific qualifier value Stimulus System Techniques Technology Testing Time Visualization Work Zebrafish autoencoder complex data conditioning data pipeline design dynamic system experimental study graphical user interface improved insight interest light weight model organism neural neural model open source optogenetics parent grant predictive modeling simulation tool transfer learning two-photon usability virtual

项目摘要

ABSTRACT Recent advances in neural recording technologies have made it possible to study increasingly large and di- verse subsets of neurons, producing a growing interest in the collective computational properties of neural pop- ulations. Ideally, causally testing these population hypotheses requires timing and selecting experimental ma- nipulations based on the current state of neural dynamics, but technical limitations have rendered this difﬁcult in practice. However, recent work on real-time preprocessing and modeling of neural data has demonstrated that up-to-the minute estimates of neural population dynamics are indeed possible, opening the door to adap-tive experiments in which the design of the task changes based on incoming data. The goal of this proposal is to disseminate these advances to the widest possible audience of systems neuroscientists by: 1) Designing and validating new methods for mapping neural states and behavior online. 2) Developing algorithms for optimally timing and selecting experimental manipulations based on these instantaneous neural and behavioral states. 3) Making improv, our platform for adaptive experiments, easier to install, use, and conﬁgure for diverse model organisms and hardware setups. By allowing researchers to test ideas online, such tools will facilitate rapid “drill-down” from the whole brain to the local circuit levels, maximizing statistical efﬁciency in limited experi- mental time and providing stronger causal inferences for neural population hypotheses, with broad implications for systems neuroscience. 1

抽象的神经记录技术的最新进展使得研究越来越大和越来越分散成为可能。神经元的子集，使人们对神经元的集体计算特性越来越感兴趣理想情况下，因果检验这些群体假设需要时机和选择实验材料基于神经动力学当前状态的 nipulations，但技术限制使得这在然而，最近关于神经数据实时预处理和建模的工作已经证明了这一点。对神经群体动态的最新估计确实是可能的，这为自适应性打开了大门任务设计根据传入数据而变化的实验该提案的目标是通过以下方式向最广泛的系统神经科学家传播这些进展：1）设计和验证在线映射神经状态和行为的新方法 2) 开发最佳算法。根据这些瞬时神经和行为状态来计时和选择实验操作 3)。使我们的自适应实验平台 improv 更易于安装、使用和配置多种模型通过允许研究人员在线测试想法，此类工具将促进快速发展。从整个大脑“深入”到局部回路层面，在有限的经验中最大化统计效率心理时间并为神经群体假设提供更强的因果推论，具有广泛的影响用于系统神经科学。 1