Real-time mapping and adaptive testing for neural population hypotheses

摘要 神经记录技术的最新进展使研究日益庞大和复杂的神经功能成为可能。 Verse神经元子集，对神经POP的集体计算特性产生了越来越大的兴趣- 恭喜你。理想情况下，对这些总体假设进行因果检验需要时机和选择实验样本。 基于神经动力学的当前状态，但技术限制使这种差异fi崇拜在 练习一下。然而，最近关于神经数据的实时预处理和建模的工作表明 神经种群动态的最新估计确实是可能的，从而打开了自适应的大门 根据输入数据改变任务设计的实验。这项提议的目标是 通过以下方式将这些进展传播给尽可能广泛的系统神经学家：1)设计和 验证在线映射神经状态和行为的新方法。2)开发优化算法 根据这些瞬时的神经和行为状态对实验操作进行计时和选择。3) Making Improv，我们的自适应实验平台，更易于安装、使用和适应不同模型的fi 生物体和硬件设置。通过允许研究人员在线测试想法，这样的工具将促进快速 从整个大脑向下钻取到局部电路级别，在有限的实验中最大限度地提高统计效率fi效率- 心理时间，并为神经群体假说提供更强的因果推断，具有广泛的意义 为了系统神经科学。 1

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