AF: Small: Collaborative Research: A Computational Theory of Brain Function

AF：小：协作研究：脑功能的计算理论

基本信息

批准号：
1909756
负责人：
Santosh Vempala
金额：
$ 20万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909756&HistoricalAwards=false
关键词：
AF Small Collaborative Research Computational

项目摘要

This project seeks to identify, explore, render rigorous, and validate one piece of the solution to the puzzle "how does the brain work?" - one of the truly fundamental and most challenging frontiers in all of science. Computation in the brain will be approached at an intermediate level of scale, far larger than that of individual neurons and synapses yet significantly smaller than that of the whole brain. The core hypothesis is that "assemblies," large and highly interconnected sets of neurons, are the engine of brain computation. Studying computation at this level is crucial for understanding higher cognitive functions, especially in humans, such as reasoning, planning, and language; and this formulation of brain computation is particularly amenable to the methodology and point of view of the theory of computation, and will further its reach. This project is quintessentially interdisciplinary, and will provide multi-faceted training to graduate and undergraduate students in Computer Science Theory, Machine Learning, and Cognitive Neuroscience and Psychology. It will develop interdisciplinary graduate courses in this particular scientific interface. The results of the project will be disseminated broadly via conferences and journals in all these disciplines, but also in colloquia and public lectures, while students of a great variety of backgrounds will participate in a cutting-edge research experience. Assemblies can be the basis of a powerful computational system involving a repertoire of operations including project, associate, and merge. These operations can be shown, through theorems and simulations, to be plausible (that is, they can be "compiled down" to the level of neurons and synapses) and useful (in the sense that they can help explain extant experimental results). The project will pursue this assembly hypothesis through: (1) expanding our modeling and our mathematical techniques of analysis for the study of assembly computation; (2) developing more accurate and efficient simulation methodology; (3) embarking on a multi-pronged exploration of the computational power of assemblies in novel modalities beyond formal computation, in particular (a) probabilistic and dynamical systems-like computation through pattern completion and (b) learning and prediction; (4) mathematical modeling and algorithmic investigation of the ways in which the dynamics and biases of synaptic connectivity, as well as assembly overlap, affect the various modes of brain computation; and, importantly, (5) functional magnetic resonance imaging (fMRI) experiments, and the analysis of the results of these experiments and extant electrocorticography (ECoG) data through novel algorithmic and machine learning techniques for the purpose of identifying evidence of assembly computation in the human brain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在识别、探索、提供严格的解决方案并验证“大脑如何工作？”这一难题的一个解决方案。 - 所有科学中真正基础且最具挑战性的前沿之一。大脑中的计算将以中等规模进行，远大于单个神经元和突触的计算，但明显小于整个大脑的计算。核心假设是“集合”，即大型且高度互连的神经元集合，是大脑计算的引擎。研究这个级别的计算对于理解更高的认知功能至关重要，尤其是人类的认知功能，例如推理、计划和语言；这种脑计算的表述特别适合计算理论的方法论和观点，并将进一步扩大其影响范围。该项目本质上是跨学科的，将为研究生和本科生提供计算机科学理论、机器学习、认知神经科学和心理学方面的多方面培训。它将在这个特定的科学界面中开发跨学科的研究生课程。该项目的成果将通过所有这些学科的会议和期刊以及座谈会和公开讲座广泛传播，而不同背景的学生将参与前沿的研究体验。程序集可以成为强大计算系统的基础，涉及一系列操作，包括项目、关联和合并。通过定理和模拟，这些操作可以被证明是合理的（也就是说，它们可以“编译”到神经元和突触的水平）和有用的（在某种意义上它们可以帮助解释现有的实验结果）。该项目将通过以下方式追求这一装配假设：（1）扩展我们的建模和数学分析技术以研究装配计算； (2)开发更准确、更高效的模拟方法； (3) 开始对形式计算之外的新模式中的程序集的计算能力进行多管齐下的探索，特别是 (a) 通过模式补全进行概率和动态系统计算以及 (b) 学习和预测；（4）对突触连接的动态和偏差以及组装重叠影响大脑计算的各种模式的方式进行数学建模和算法研究；重要的是，（5）功能磁共振成像（fMRI）实验，以及通过新颖的算法和机器学习技术对这些实验结果和现有皮层电图（ECoG）数据进行分析，以识别组装计算的证据该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。