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; 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.

该项目旨在识别，探索，渲染严格，并验证一种解决难题的解决方案“大脑如何工作？” - 整个科学中真正基本和最具挑战性的边界之一。大脑中的计算将以中等水平进行，远大于单个神经元和突触的计算，但明显小于整个大脑。核心假设是，“组装”是大脑计算的大型且高度互连的神经元集。在此级别上研究计算对于理解更高的认知功能至关重要，尤其是在人类中，例如推理，计划和语言。大脑计算的这种表述尤其适合计算理论的方法和观点，并将进一步覆盖其范围。该项目是典型的跨学科，将为计算机科学理论，机器学习以及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。它将在此特定的科学界面中发展跨学科的研究生课程。该项目的结果将通过所有这些学科的会议和期刊广泛传播，但也将在校长和公开讲座中传播，而各种背景的学生将参与尖端的研究经验。组件可以成为强大的计算系统的基础，该计算系统涉及包括项目，合伙人和合并在内的运营曲目。可以通过定理和模拟显示这些操作是合理的（也就是说，可以将它们“汇编”到神经元和突触的水平）和有用（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）扩大我们的建模和我们的数学分析技术来追求这一集会假设，以研究组装计算； （2）开发更准确，更有效的仿真方法； （3）在正式计算以外的新型方式中启动对组件的计算能力的多管齐下探索，尤其是（a）通过模式完成和（b）学习和预测，概率和动态系统式计算； （4）对突触连通性的动力学和偏见以及组装重叠的方式的数学建模和算法研究影响了大脑计算的各种模式； （5）（5）通过新型算法和机器学习技术对这些实验和现有电视图（ECOG）数据进行功能磁共振成像（fMRI）实验的分析，目的是确定人类奖励的范围，以识别NSF的构建范围，以识别NSF的构建范围，以表明NSF的构建范围，以识别NSF的构建范围。影响审查标准。