Simultaneous localisation and mapping (SLAM) algorithms are commonly used in robotic systems for learning maps of novel environments. Brains also appear to learn maps, but the mechanisms are not known and it is unclear how to infer these maps from neural activity data. We present BrainSLAM; a method for performing SLAM using only population activity (local field potential, LFP) data simultaneously recorded from three brain regions in rats: hippocampus, prefrontal cortex, and parietal cortex. This system uses a convolutional neural network (CNN) to decode velocity and familiarity information from wavelet scalograms of neural local field potential data recorded from rats as they navigate a 2D maze. The CNN's output drives a RatSLAM-inspired architecture, powering an attractor network which performs path integration plus a separate system which performs `loop closure' (detecting previously visited locations and correcting map aliasing errors). Together, these three components can construct faithful representations of the environment while simultaneously tracking the animal's location. This is the first demonstration of inference of a spatial map from brain recordings. Our findings expand SLAM to a new modality, enabling a new method of mapping environments and facilitating a better understanding of the role of cognitive maps in navigation and decision making.

翻译：同步定位与地图构建（SLAM）算法常用于机器人系统，以学习陌生环境的地图。大脑似乎也能学习地图，但其机制尚不清楚，且如何从神经活动数据中推断这些地图也仍未明确。我们提出BrainSLAM——一种仅利用大鼠三个脑区（海马体、前额叶皮层和顶叶皮层）同步记录的群体活动数据（局部场电位）进行SLAM的方法。该系统使用卷积神经网络（CNN），从大鼠在二维迷宫中导航时记录的神经局部场电位数据的小波尺度图中解码速度与熟悉性信息。CNN的输出驱动一种受RatSLAM启发的架构，该架构包含一个执行路径整合的吸引子网络，以及一个独立的"闭环检测"系统（用于识别先前访问过的位置并纠正地图混叠误差）。这三个组件协同工作，能够在跟踪动物位置的同时构建环境的高保真表征。这是首次从脑记录中推断空间地图的演示。我们的研究成果将SLAM拓展至新的模态，为环境地图构建提供新方法，并促进对认知地图在导航与决策中作用的更深入理解。