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。

BrainSLAM：神经群体活动数据上的 SLAM

BrainSLAM: SLAM on Neural Population Activity Data

This article presents a review of computational methods for connectivity
inference from neural activity data derived from multi-electrode recordings or
fluorescence imaging. We first identify biophysical and technical challenges in
connectivity inference along the data processing pipeline. We then review
connectivity inference methods based on two major mathematical foundations,
namely, descriptive model-free approaches and generative model-based
approaches. We investigate representative studies in both categories and
clarify which challenges have been addressed by which method. We further
identify critical open issues and possible research directions.

本文综述了从多电极记录或荧光成像获得的神经活动数据中推断连通性的计算方法，探讨了描述性无模型方法和生成性基于模型方法两个数学基础，并分析了代表性研究，最后指出了关键性的未解决问题和可能的研究方向。