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

AF：小：协作研究：脑功能的计算理论

• 批准号: 1910700
• 负责人: Christos Papadimitriou
• 金额: $ 20万
• 依托单位: Columbia University
• 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 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910700&HistoricalAwards=false
• 关键词: AF; Small; Collaborative; Research; Computational

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.

这个项目旨在识别，探索，呈现严格，并验证一个难题的解决方案“大脑是如何工作的？“-所有科学中真正基本和最具挑战性的前沿之一。大脑中的计算将在一个中等规模的水平上进行，远大于单个神经元和突触的计算，但远小于整个大脑的计算。其核心假设是，“集合体”，即大型且高度互连的神经元集合，是大脑计算的引擎。在这个层面上研究计算对于理解更高的认知功能至关重要，特别是在人类中，例如推理，规划和语言;这种大脑计算的公式特别适合计算理论的方法和观点，并将进一步扩大其范围。该项目是典型的跨学科，将为计算机科学理论，机器学习，认知神经科学和心理学的研究生和本科生提供多方面的培训。它将在这一特定的科学界面上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广泛传播，也将通过座谈会和公开讲座广泛传播，而各种背景的学生将参加尖端的研究经验。程序集可以是功能强大的计算系统的基础，该系统涉及一系列操作，包括项目、关联和合并。通过定理和模拟，这些运算可以被证明是合理的（也就是说，它们可以被“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存的实验结果）。本计画将透过下列方式来实现此装配假设：（1）扩充我们的模型与数学分析技术，以研究装配计算;（2）发展更精确且有效的模拟方法;（3）在形式计算之外的新模式中，对汇编的计算能力进行多管齐下的探索，特别是（a）通过模式完成概率和动态系统类计算和（B）学习和预测;（4）对突触连接的动态和偏差的方式进行数学建模和算法研究，以及组装重叠，影响大脑计算的各种模式;以及，重要的是，（5）功能性磁共振成像（fMRI）实验，并对这些实验结果和现存的皮层电图（ECoG）进行了分析通过新的算法和机器学习技术来识别人类大脑中组装计算的证据。这该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。