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.

人类有非凡的能力获得存储在语义记忆中的丰富的概念库， 这在“学习精益”中可能是令人遗憾的，其促进了快速的新学习或甚至一次性学习。 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智力。潜在的大脑机制完全是 这是一个未知的问题，代表了当今神经科学前沿的一个广泛开放的问题。本 计算项目与本申请的实验项目一起推测，其主要目标是 阐明了快速学习的神经回路基础。进展是这一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对高级认知的神经科学理解方面向前迈进了一步。 我们的建模方法集成了灵长类动物大脑的大规模电路建模， 介观连接和训练递归神经网络来执行认知任务。连同 在这个应用中，我们将开发工具来描述和阐明神经种群 单次试验中的动力学，这对于快速学习的神经生理学分析至关重要（即使是一次性的 学习），而无需在稳态情况下对许多重复试验求平均。主要的假设是， 学会学习取决于感觉运动表征的抽象的形成，例如 任务结构或“图式”，这表现在神经表征从海马体到大脑皮层的转移中。 前额叶皮质;这种概念表示通过有效改变来实现快速的未来学习 低维子空间内的连接权重。这个假设将使用状态空间进行检验 递归神经网络动力学的分析和降维。 目标1是提出一个基于介观连通性的多区域神经网络模型， 分类、灵活的感觉-运动映射和物体-位置关联的快速学习。该模型将 通过与类别学习的行为数据进行比较，进行系统的测试和验证， 联想学习任务。目的二是揭示神经元群体动力学和神经回路机制， 在单次试验中学习，使用状态空间分析和识别神经种群动力学的子空间 以及可以对应于语义记忆的形成的连接权重的子空间。目标3 将剖析HPC，PFC，PPC的不同作用及其快速发展背后的动态相互作用 学习，通过在学习过程的不同时间点模拟“区域损伤”。一个尖峰网络版本的 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。 这一领域的进展不仅对学习和记忆的神经科学很重要， 也可能对人工智能的未来发展产生重大影响，并对人类的未来发展产生重要影响。 语义记忆缺陷的大脑机制，这是额颞叶痴呆症的核心。