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

这个项目试图识别、探索、呈现和验证谜题“大脑是如何工作的？”--这是所有科学中真正基础和最具挑战性的前沿之一。大脑中的计算将在中等尺度上进行，远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”，即大的、高度相互关联的神经元集合，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，特别是人类的高级认知功能，如推理、规划和语言是至关重要的；大脑计算的这种表述特别服从计算理论的方法论和观点，并将进一步扩大其影响范围。这个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。它将在这一特殊的科学接口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，但也将通过座谈会和公开讲座传播，同时各种背景的学生将参与尖端研究体验。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，这些操作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(从某种意义上说，它们可以帮助解释现有的实验结果)。该项目将通过：(1)扩展我们的建模和我们的数学分析技术来研究组装计算；(2)开发更准确和有效的模拟方法；(3)在形式计算之外，多管齐下地探索组装的计算能力，特别是(A)通过模式完成和(B)学习和预测进行类似概率和动力系统的计算；(4)对突触连接的动力学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；以及，重要的是，(5)功能磁共振成像(FMRI)实验，以及通过新的算法和机器学习技术对这些实验的结果和现有的皮层脑电(ECoG)数据进行分析，目的是识别人类大脑中组装计算的证据。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.