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

空间序列学习是一个复杂的认知过程，它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。该项目的目标是通过将大鼠实验研究获得的信息与计算模型和自主移动机器人相结合，更好地了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体和前额叶皮质(PFC)在内的大脑区域。最近在大鼠身上的研究表明，这两个区域的神经元会在短时间内自发重新激活。在长期记忆巩固的背景下，睡眠期间的重新激活受到了极大的关注。这个项目的重点是在清醒状态下重放的作用，因为动物在同一阶段通过多次试验进行学习。假设是，在海马体中产生这些短的活动序列允许在PFC中进行全局空间序列学习。这项拟议的工作涉及开发一个能够形成空间导航序列的海马体-PFC网络的集成模型，该模型包括：1)海马体中记忆形成的重放驱动模型和2)PFC中使用所谓的水库计算的空间序列学习模型。PFC储备池将由大量相互连接的神经元池组成，这些神经元池通过混响动态处理信息。它将把海马体重放序列合并成更大的空间序列，稍后可能会被原始序列的子集召回。这项拟议的工作有望对重放在序列学习等复杂任务中记忆获得中的作用产生一种新的机械性理解。这种理解将被利用，并在机器人平台上进行测试。拟议工作的原创性贡献包括1)使用海马体回放来创建有效轨迹的小块，2)使用储备库计算来使用海马体模型的输出来学习空间序列，3)使用行为大鼠的电生理数据来约束和测试该模型，以及4)在机器人的具身认知框架中使用所得到的模型。法国国家研究机构(ANR)正在资助一个配套项目。

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).