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.

人类具有出色的能力，可以获取存储在语义记忆中的概念的丰富曲目， 可以将其部署在“学习倾斜”中，从而有助于快速的新学习甚至一声学习。 非人类动物也被“学习学习”。另一方面，有证据表明 灵长类动物但没有啮齿动物具有这种心理能力。潜在的大脑机制是完全 在当今神经科学的边界，未知并代表了一个广泛的公开问题。现在 计算项目在该应用程序的实验项目中的概念上，其主要目标是 阐明快速学习的神经回路基础。进度是这个研究方向将代表一个主要 向前迈进，弥合非人类灵长类动物和对更高认知的人类神经科学的理解。 我们的建模方法基于测量 介观连通性和训练复发性神经网络以执行认知任务。与 在此应用中提出的实验，我们将开发描述和阐明神经元种群的工具 单个试验中的动力学，这对于快速学习的神经生理学分析至关重要（甚至一次） 学习），而无需在稳态情况下平均许多重复试验。主要假设是 学习学习取决于传感运动表示的抽象的形成，例如 任务结构或“架构”，这表现在神经表示从海马到 前额叶皮层；这种概念表示可以通过有效的更改来快速学习 连接权重在低维子空间内。该假设将使用状态空间进行检验 复发性神经网络动力学的分析和维度降低。 目标1将是推进基于介观连接的多区域神经网络模型 类别的快速学习，灵活的感觉运动映射和对象位置关联。该模型将 通过与类别学习的行为数据进行系统测试和验证 协会学习任务。 AIM 2将是发现快速的神经种群动态和电路机制 在单个试验中学习，使用状态空间分析并识别神经种群动态的子空间 以及连接权重的子空间，可能对应于语义内存的形成。目标3 将剖析HPC，PFC，PPC及其动态相互作用的差异作用 通过在学习过程的不同时间点模拟“区域病变”来学习。尖峰网络版本的 我们的模型将使我们能够揭示美主的动态相互作用及其在快速学习中的作用。 该领域的进步不仅对学习和记忆的神经科学很重要，而且对 还可能对AI的未来发展产生重大影响，并散发出对 语义记忆中防御能力的大脑机制，这是额颞痴呆的核心。