NCS-FO: Collaborative Research: Flexible Rule-Based Categorization in Neural Circuits and Neural Network Models

NCS-FO：协作研究：神经电路和神经网络模型中基于规则的灵活分类

• 批准号: 1631586
• 负责人: Xiao-Jing Wang
• 金额: $ 41.42万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631586&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Flexible

Categorization is the brain's ability to recognize the meaning of objects and events in our environment, and is an essential cognitive process underlying decision making. Categorical decisions are often flexible, and depend on the demands on the task at hand. The current project aims to understand the brain mechanisms which underlie flexible categorical decision making, as well as computational algorithms for making such decisions my artificially intelligent systems. Experiments will record from ensembles of cortical neurons during flexible categorization tasks. Computational modeling work will train recurrent neural networks to perform the same flexible categorization tasks used in the experiments, with parameters of the model inspired by the experimental data. This will result in a greater understanding of the neural mechanisms underlying categorization and decision making, as well as improvements in computational algorithms for flexible categorization by artificially intelligent systems. The broader impacts of the project include substantial training opportunities for undergraduates, Ph.D. students, and postdoctoral researchers in both experimental and computational approaches to flexible decision making. The project will also generate new experimental data and computational tools that will be shared with the broader scientific community. This project combines multi-channel neurophysiological recordings and neural circuit modeling to investigate the neural circuit mechanisms of flexibility and generalization in visual categorization. The project leverages a collaboration by the researchers that has proven fruitful in our previous joint research on category learning. The focus of the present project is on flexible task switching between discrimination and categorization, and between categorization rules, in the behavioral, experimental, and computational work. The task paradigms will also directly test the 'exemplar model' of categorization from cognitive psychology, linking behavioral models to neural circuit processes. The project will develop a novel modeling framework, based on training recurrent neural networks to learn to perform multiple tasks. This approach offers a potentially powerful data analysis tool and conceptualization of neural circuit computation in terms of neural population trajectories in a high-dimensional state space, and this perspective is urgently needed to analyze simultaneous recording from many single neurons during performance of complex cognitive tasks, a major thread of modern Data-Intensive Neuroscience and Cognitive Science.

范畴化是大脑在我们的环境中识别物体和事件的意义的能力，是决策的基本认知过程。分类决策通常是灵活的，并且取决于手头任务的要求。目前的项目旨在了解灵活的分类决策背后的大脑机制，以及人工智能系统做出此类决策的计算算法。实验将记录在灵活的分类任务的皮质神经元的合奏。计算建模工作将训练递归神经网络来执行与实验中使用的相同的灵活分类任务，模型的参数受实验数据的启发。这将导致对分类和决策的神经机制的更好理解，以及人工智能系统灵活分类的计算算法的改进。该项目的更广泛影响包括为本科生、博士生和研究生提供大量培训机会。学生和博士后研究人员在实验和计算方法灵活决策。该项目还将产生新的实验数据和计算工具，与更广泛的科学界共享。本研究结合多通道神经生理记录和神经回路模型，探讨视觉分类中灵活性和泛化的神经回路机制。该项目利用了研究人员的合作，这在我们之前关于类别学习的联合研究中已经证明是富有成效的。本项目的重点是灵活的任务切换之间的歧视和分类，分类规则之间，在行为，实验和计算工作。任务范式还将直接测试认知心理学的分类“范例模型”，将行为模型与神经回路过程联系起来。该项目将开发一个新的建模框架，基于训练递归神经网络来学习执行多个任务。这种方法提供了一个潜在的强大的数据分析工具和概念化的神经电路计算的神经种群轨迹在一个高维的状态空间，这一观点是迫切需要分析同时记录从许多单个神经元在执行复杂的认知任务，现代数据密集型神经科学和认知科学的一个主要线索。

项目成果

Reward-based training of recurrent neural networks for cognitive and value-based tasks

• DOI: 10.7554/elife.21492
• 发表时间: 2016-08
• 期刊: eLife
• 影响因子: 7.7
• 作者: H. F. Song;G. R. Yang;Xiao-Jing Wang;Xiao-Jing Wang