BrainSLAM: SLAM on Neural Population Activity Data
This work expands SLAM to neural data, enabling new mapping methods and better understanding of cognitive maps in navigation, but it is incremental as it adapts existing SLAM techniques to a new modality.
The authors tackled the problem of inferring spatial maps from neural population activity by developing BrainSLAM, a method that uses a CNN to decode velocity and familiarity from LFP data in rats navigating a maze, combined with a RatSLAM-inspired architecture to construct faithful environment representations and track location, achieving the first demonstration of such inference from brain recordings.
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.