CDI-Type I: New Information-theoretic Methods for Analysis of Neuronal Ensembles

CDI-I 型：用于分析神经元集成的新信息论方法

• 批准号: 0941343
• 负责人: Michael Gastpar
• 金额: $ 65万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941343&HistoricalAwards=false
• 关键词: CDI; Type; New; Information; theoretic

Information theory has been used as an organizing principle in neuroscience for several decades. Estimates of the mutual information between signals acquired in neurophysiological experiments are believed to yield insights into the structure of the underlying signal processing architectures. The experimental side of the field has undergone an impressive paradigmatic shift over the last few years, including simultaneous recordings from neuron populations and brain-machine interfaces (BMIs). However, on the theoretical side, only minor modifications to the theory were made. This project aims at transforming the theoretical backdrop of systems neuroscience in general and neural ensemble electrophysiology in particular. To this end, it levies three recent and emerging theoretical advances in the network sciences: (1) the tremendous recent progress concerning the behavior and information-theoretic performance limits of communication networks, fueled by the resounding success of wireless communication; (2) we have recently developed a new information theory of joint computation/communication, showing that for networks with interference, joint computation/communication can be executed much more efficiently - an exponential energy savings as a function of the number of agents involved in the computation - than separate computation and communication (as is classically done in communication systems). Our hypothesis is that such a significant gap must have repercussions in neural systems, and we develop new information measures to look for places where indeed joint computation/communication could be a crucial component of the neural signal processing. Finally, (3), we exploit a new information-theoretic end-to-end perspective on communication systems. All of these methods are applicable only thanks to new emerging data sets pertaining to chronic, simultaneous recordings from large populations of neurons across multiple brain regions. The further acquisition and development of these data sets is therefore an integral part of this research project.This project introduces a set of new information measures that are truly multivariate, and thus a perfect fit for the emerging multi-neuron data sets, and particularly for BMI. This involves both natural extensions of existing information and redundancy measures as well as a new computational information measure that assesses not just the quality of information transfer, but also the computational potential offered by a population of neurons. The project aims to develop a new set of theories, techniques, and methods for dealing with and understanding multi-neuron recordings across spatiotemporal dimensions. If successful, this will provide ideas for new experiments and sharpen our understanding of the connections between communication and computation.

几十年来，信息论一直被用作神经科学的组织原理。人们相信，对神经生理学实验中获取的信号之间的互信息的估计可以深入了解底层信号处理架构的结构。在过去的几年里，该领域的实验方面经历了令人印象深刻的范式转变，包括神经元群体和脑机接口（BMI）的同步记录。然而，在理论方面，仅对理论进行了微小的修改。该项目旨在改变一般系统神经科学，特别是神经集合电生理学的理论背景。为此，它引用了网络科学中最近出现的三项理论进展：（1）在无线通信的巨大成功的推动下，在通信网络的行为和信息论性能限制方面取得了巨大的最新进展； （2）我们最近开发了一种新的联合计算/通信信息理论，表明对于有干扰的网络，联合计算/通信可以更有效地执行——指数级节能，作为参与计算/通信的代理数量的函数。计算 - 而不是单独的计算和通信（如通信系统中的经典做法）。我们的假设是，如此巨大的差距一定会对神经系统产生影响，并且我们开发了新的信息测量方法来寻找联合计算/通信确实可能成为神经信号处理的关键组成部分的地方。最后，（3），我们利用了通信系统的新信息论端到端视角。所有这些方法的适用仅归功于与跨多个大脑区域的大量神经元的长期、同时记录有关的新出现的数据集。因此，这些数据集的进一步获取和开发是该研究项目的一个组成部分。该项目引入了一组真正多元的新信息度量，因此非常适合新兴的多神经元数据集，特别是对于体重指数。这涉及现有信息和冗余测量的自然扩展，以及新的计算信息测量，该测量不仅评估信息传输的质量，还评估神经元群体提供的计算潜力。该项目旨在开发一套新的理论、技术和方法来处理和理解跨时空维度的多神经元记录。如果成功，这将为新的实验提供思路，并加深我们对通信和计算之间联系的理解。