Neural circuit theory and trained recurrent network modeling of rapid learning

人类具有非凡的能力来获得存储在语义记忆中的丰富的概念曲目， 这可以在“学会学习”中受到谴责，它有助于快速学习新知识，甚至是一次性学习。 非人类的动物也被赋予了“学会学习”的能力；另一方面，有证据表明 灵长类动物，而不是啮齿动物，拥有这种智力能力。潜在的大脑机制完全是 这是一个未知的问题，代表着当今神经科学前沿的一个广泛开放的问题。现在 与此应用程序的实验项目一起推测，计算项目的主要目标是 阐明快速学习的神经回路基础。这一进展将代表这一研究方向的重大意义 在非人类灵长类动物和人类对高等认知的神经科学理解方面向前迈进一步。 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 中观连通性和训练递归神经网络执行认知任务。与 在这一应用中，我们将开发工具来描述和阐明神经细胞群体 单次试验中的动力学，这对于快速学习的神经生理学分析(即使是一次性的)至关重要 学习)，而无需在稳定状态下进行多次重复试验的平均值。主要的假设是 学会学习依赖于感觉-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转移到 前额叶皮质；这种概念表征通过有效地改变 低维子空间内的连接权重。这一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与降维。 目标1将是提出一种基于介观连通性的多区域神经网络模型 在分类、灵活的感觉-运动映射和物体-位置关联方面快速学习。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。目标2将揭示快速的神经群动力学和电路机制。 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子空间。目标3 将剖析HPC、PFC、PPC的不同角色及其快速背后的动态相互作用 学习，通过模拟学习过程中不同时间点的“区域损伤”。尖峰网络版的 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，而且 也可能对人工智能的未来发展产生重大影响，并对 语义记忆缺陷的大脑机制，这是额叶-颞叶痴呆的核心。

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.