Neural circuit theory and trained recurrent network modeling of rapid learning

神经回路理论与快速学习的训练循环网络建模

• 批准号: 10456065
• 负责人: XIAO-JING WANG
• 金额: $ 24.65万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10456065
• 关键词: Algorithms; Animals; Area; Artificial Intelligence; Back; Base of the Brain; Behavioral; Biological; Brain; Categories; Code; Cognition; Computer Simulation; Data; Development; Dimensions; Frontotemporal Dementia; Future; Goals; Hippocampus (Brain); Human; Knowledge; Learning; Lesion; Location; Machine Learning; Measures; Memory; Metaplasia; Modeling; Monkeys; Neural Network Simulation; Neurons; Neurosciences; Population Dynamics; Prefrontal Cortex; Primates; Principal Component Analysis; Process; Protocols documentation; Psyche structure; Psychological reinforcement; Recurrence; Research; Role; Sampling; Semantic memory; Semantics; Sensory; Stimulus; Structure; Study models; Synapses; Testing; Thinness; Time; Training; Weight; base; brain research; classical conditioning; cognitive task; computational neuroscience; computer framework; experimental study; flexibility; frontier; insight; learning algorithm; network models; neural circuit; neural network; neurophysiology; nonhuman primate; recurrent neural network; relating to nervous system; supervised learning; theories; tool; two-dimensional

Humans have remarkable ability to acquire a rich repertoire of concepts stored in semantic memory, which can be deplored in “learning to lean” that facilitates rapid new learning or even one-shot learning. Nonhuman animals are also endowed with “learning to learn”; on the other hand, there is evidence that primates but not rodents possess this mental capability. The underlying brain mechanisms are completely unknown and represent a widely open question at the frontier of Neuroscience today. The present computational project, in conjecture with the experimental projects of this application, has the primary goal of elucidating the neural circuit basis of rapid learning. Progress is this research direction will represent a major step forward in bridging nonhuman primate and human neuroscientific understanding of higher cognition. Our modeling approach integrates large-scale circuit modeling of primate brain based on measured mesoscopic connectivity and training recurrent neural networks to perform cognitive tasks. Together with the proposed experiments in this application, we will develop tools to describe and elucidate neural population dynamics in single trials, which is crucial for neurophysiological analysis of rapid learning (even one-shot learning) without averaging over many repetitive trials in a steady state situation. The main hypothesis is that learning to learn depends on the formation of an abstraction of sensori-motor representations, such as that of task structure or “schema”, which is manifested in a shift of neural representation from the hippocampus to the prefrontal cortex; this conceptual representation enables rapid future learning by efficient changes of connection weights within a low dimensional subspace. This hypothesis will be tested using the state space analysis and dimensionality reduction of the recurrent neural network dynamics. Aim 1 will to be to advance a mesoscopic connectivity-based multi-regional neural network model for rapid learning in categorization, flexible sensori-motor mapping and object-location association. The model will be systematically tested and validated by comparison with behavioral data from category learning and associative learning tasks. Aim 2 will be to uncover neural population dynamics and circuit mechanism of rapid learning in single trials, using state-space analysis and identifying a subspace of neural population dynamics as well as a subspace of connection weights that may correspond to the formation of semantic memory. Aim 3 will be to dissect the differential roles of HPC, PFC, PPC and their dynamical interactions underlying rapid learning, by simulating “area lesion” at different time points of a learning process. A spiking network version of our model will enable us to uncover inter-areal dynamical interactions and their role in rapid learning. Advances in this area would not only be important for the Neuroscience of learning and memory, but also have potentially major implications for the future development of AI, and for shedding insights into the brain mechanism of deficits in semantic memory, which is at the core of fronto-temporal dementia.

人类具有非凡的能力来获取存储在语义记忆中的丰富概念， 这在促进快速新学习甚至一次性学习的“学习精益”中可能会令人遗憾。 非人类动物也被赋予了“学会学习”的能力；另一方面，有证据表明 灵长类动物而不是啮齿类动物拥有这种心理能力。基本的大脑机制是完全 未知，代表了当今神经科学前沿的一个广泛开放的问题。现在的 根据本应用程序的实验项目推测，计算项目的主要目标是 阐明快速学习的神经回路基础。进展是这个研究方向将代表一个重大 在连接非人类灵长类动物和人类神经科学对高级认知的理解方面向前迈出了一步。 我们的建模方法集成了基于测量的灵长类大脑的大规模电路建模 介观连接和训练循环神经网络来执行认知任务。与 在本应用程序中提出的实验中，我们将开发工具来描述和阐明神经群体 单次试验的动力学，这对于快速学习（甚至是一次性的）的神经生理学分析至关重要 学习），而不是在稳态情况下对多次重复试验进行平均。主要假设是 学会学习取决于感觉运动表征的抽象形成，例如 任务结构或“图式”，表现为神经表征从海马体到大脑的转变 前额皮质；这种概念表示能够通过有效的改变来实现快速的未来学习 低维子空间内的连接权重。该假设将使用状态空间进行检验 循环神经网络动力学的分析和降维。 目标 1 是提出一种基于介观连通性的多区域神经网络模型 快速学习分类、灵活的感觉运动映射和物体位置关联。该模型将 通过与类别学习的行为数据进行比较来进行系统的测试和验证 联想学习任务。目标 2 是揭示神经群体动态和快速神经回路机制 在单次试验中学习，使用状态空间分析并识别神经群体动态的子空间 以及可能对应于语义记忆的形成的连接权重子空间。目标 3 将剖析 HPC、PFC、PPC 的不同作用以及它们在快速 通过在学习过程的不同时间点模拟“区域病变”来进行学习。秒杀网络版 我们的模型将使我们能够揭示区域间的动态相互作用及其在快速学习中的作用。 这一领域的进步不仅对学习和记忆的神经科学很重要，而且对 也对人工智能的未来发展以及对人工智能的洞察产生潜在的重大影响。 语义记忆缺陷的大脑机制，这是额颞叶痴呆的核心。