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.

使用大鼠的神经活动数据，通过从大脑记录的局部场电位数据中解码速度和熟悉度信息，并利用 RatSLAM 启发的体系结构构建了空间地图的方法，将 SLAM 算法扩展至新的模态，从而促进了对认知地图在导航和决策中的作用的更好理解。