CRCNS US-French Research Proposal: Collaborative Research: A replay-driven model of spatial sequence learning in the Hippocampus-PFC network using reservoir computing

CRCNS 美国-法国研究提案：合作研究：使用储层计算的海马-PFC 网络中重放驱动的空间序列学习模型

• 批准号: 1429929
• 负责人: Jean-Marc Fellous
• 金额: $ 36.1万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1429929&HistoricalAwards=false
• 关键词: CRCNS; US; French; Research; Proposal

Spatial sequence learning is a complex cognitive process that enables animals and humans to reliably navigate between different locations in a specific order. The goal of this project is to provide a better understanding of how spatial sequence navigation is learned and optimized by integrating information obtained from experimental studies in rats with computational models and autonomous mobile robots.Spatial sequence learning has been shown to involve brain areas including the hippocampus and the prefrontal cortex (PFC). Recent studies in the rat have shown that neurons in these two areas spontaneously re-activate in short sequences. Much attention has been paid to reactivation during sleep in the context of long-term memory consolidation. The focus of this project is on the role of replay during the awake state, as the animal is learning across multiple trials during the same session. The hypothesis is that the generation of these short sequences of activity in hippocampus allows for global spatial sequence learning in the PFC. The proposed work involves the development of an integrated model of the hippocampus-PFC network that is able to form spatial navigation sequences incorporating: 1) a replay-driven model for memory formation in the hippocampus and 2) a model of spatial sequence learning in the PFC that uses what is known as reservoir computing. The PFC reservoir will consist of large pools of interconnected neural elements that process information dynamically through reverberations. It will consolidate hippocampal replay sequences into larger spatial sequences that may be later recalled by subsets of the original sequences. The proposed work is expected to generate a new mechanistic understanding of the role of replay in memory acquisition in complex tasks such as sequence learning. That understanding will be leveraged and tested on robotic platforms. Original contributions of the proposed work include 1) the use of hippocampal replay to create small chunks of valid trajectories, 2) the use of reservoir computing to learn spatial sequences using the outputs of the hippocampus model, 3) constraining and testing of the model using electrophysiological data in behaving rats and 4) the use of the resulting model in the embodied-cognitive framework of a robot.A companion project is being funded by the French National Research Agency (ANR).

空间序列学习是一个复杂的认知过程，使动物和人类能够以特定的顺序在不同的位置之间可靠地导航。本项目的目标是通过整合大鼠实验研究中获得的信息，利用计算模型和自主移动的机器人，更好地理解空间序列导航是如何学习和优化的。空间序列学习已被证明涉及包括海马和前额叶皮层（PFC）在内的大脑区域。最近对大鼠的研究表明，这两个区域的神经元在短时间内自发地重新激活。在长期记忆巩固的背景下，人们对睡眠中的再激活给予了很大的关注。这个项目的重点是在清醒状态下重放的作用，因为动物在同一个会话期间学习多个试验。该假设是，这些短序列的活动在海马体中的生成允许在PFC的全球空间序列学习。拟议的工作涉及开发一个综合模型的海马-PFC网络，能够形成空间导航序列，包括：1）海马体中记忆形成的重放驱动模型; 2）前额叶皮层中空间序列学习的模型，使用所谓的水库计算。PFC水库将由大量相互连接的神经元组成，这些神经元通过混响动态处理信息。它将海马重放序列合并成更大的空间序列，这些序列以后可能被原始序列的子集调用。这项工作有望产生一个新的机械理解的作用，在复杂的任务，如序列学习的记忆获取重放。这种理解将在机器人平台上得到利用和测试。所提出的工作的原始贡献包括1）使用海马重放来创建小块的有效轨迹，2）使用水库计算来使用海马模型的输出来学习空间序列，3）在行为大鼠中使用电生理学数据约束和测试模型，以及4）在具体实施中使用所得模型。法国国家研究机构（ANR）正在资助一个配套项目。