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

分类是大脑识别环境中对象和事件的意义的能力，是进行决策的基本认知过程。明确的决定通常是灵活的，取决于手头任务的需求。目前的项目旨在了解灵活的分类决策背后的大脑机制，以及在我的人工智能系统中做出此类决策的计算算法。实验将在灵活的分类任务中记录大脑皮层神经元的集合。计算建模工作将训练递归神经网络执行与实验中使用的相同灵活分类任务，模型的参数受到实验数据的启发。这将导致对分类和决策背后的神经机制的更好理解，以及人工智能系统灵活分类计算算法的改进。该项目的更广泛影响包括为本科生、博士生和博士后研究人员提供大量的培训机会，包括灵活决策的实验和计算方法。该项目还将产生新的实验数据和计算工具，将与更广泛的科学界共享。该项目结合了多通道神经生理学记录和神经电路建模，以研究视觉分类中灵活性和泛化的神经电路机制。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被证明是卓有成效的。本项目的重点是在行为、实验和计算工作中，在区分和分类之间以及分类规则之间进行灵活的任务切换。任务范式还将直接测试认知心理学中的范畴化‘样本模型’，将行为模型与神经回路过程联系起来。该项目将开发一种新的建模框架，基于训练递归神经网络来学习执行多项任务。这种方法提供了一种潜在的强大的数据分析工具，并根据高维状态空间中的神经元种群轨迹对神经电路计算进行概念化，并且迫切需要这种视角来分析复杂认知任务执行过程中来自多个单个神经元的同时记录，这是现代数据密集型神经科学和认知科学的主线。

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.