Humans and animals can recognize latent structures in their environment and
apply this information to efficiently navigate the world. Several recent works
argue that the brain supports these abilities by forming neural representations
that encode such latent structures in flexible, generalizable ways. However, it
remains unclear what aspects of neural population activity are contributing to
these computational capabilities. Here, we develop an analytical theory linking
the mesoscopic statistics of a neural population's activity to generalization
performance on a multi-task learning problem. To do this, we rely on a
generative model in which different tasks depend on a common, unobserved latent
structure and predictions are formed from a linear readout of a neural
population's activity. We show that three geometric measures of the population
activity determine generalization performance in these settings. Using this
theory, we find that experimentally observed factorized (or disentangled)
representations naturally emerge as an optimal solution to the multi-task
learning problem. We go on to show that when data is scarce, optimal codes
compress less informative latent variables, and when data is abundant, optimal
codes expand this information in the state space. We validate predictions from
our theory using biological and artificial neural network data. Our results
therefore tie neural population geometry to the multi-task learning problem and
make normative predictions of the structure of population activity in these
settings.

人和动物可以认识到环境中的潜在结构，并将这些信息应用于有效地导航世界。本论文开发了一个分析理论，将神经群体活动的中等统计特性与多任务学习问题上的泛化性能相联系。利用这个理论，我们发现实验观察到的因素化（或解缠）表示自然地成为多任务学习问题的最优解，同时验证了来自生物和人工神经网络数据的理论预测。这些结果将神经群体几何学与多任务学习问题联系起来，并对这些场景中的群体活动结构进行正式预测。

神经种群几何和任务共享潜在结构的最优编码

Neural Population Geometry and Optimal Coding of Tasks with Shared  Latent Structure

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

We introduce a novel data-driven approach to discover and decode features in
the neural code coming from large population neural recordings with minimal
assumptions, using cohomological feature extraction. We apply our approach to
neural recordings of mice moving freely in a box, where we find a circular
feature. We then observe that the decoded value corresponds well to the head
direction of the mouse. Thus we capture head direction cells and decode the
head direction from the neural population activity without having to process
the behaviour of the mouse. Interestingly, the decoded values convey more
information about the neural activity than the tracked head direction does,
with differences that have some spatial organization. Finally, we note that the
residual population activity, after the head direction has been accounted for,
retains some low-dimensional structure which is correlated with the speed of
the mouse.

该研究提出了一种新的数据驱动方法来发现和解码来自大规模神经记录的神经编码中的特征，利用同调特征提取。研究者将该方法应用于捕获自由活动老鼠的神经记录，发现了一个环形特征，并成功解码老鼠头部方向。研究者发现，解码的值传达了更多有关神经活动的信息，并与老鼠的速度相关。