We investigate the boundary between chemotaxis driven by spatial estimation
of gradients and chemotaxis driven by temporal estimation. While it is well
known that spatial chemotaxis becomes disadvantageous for small organisms at
high noise levels, it is unclear whether there is a discontinuous switch of
optimal strategies or a continuous transition exists. Here, we employ deep
reinforcement learning to study the possible integration of spatial and
temporal information in an a priori unconstrained manner. We parameterize such
a combined chemotactic policy by a recurrent neural network and evaluate it
using a minimal theoretical model of a chemotactic cell. By comparing with
constrained variants of the policy, we show that it converges to purely
temporal and spatial strategies at small and large cell sizes, respectively. We
find that the transition between the regimes is continuous, with the combined
strategy outperforming in the transition region both the constrained variants
as well as models that explicitly integrate spatial and temporal information.
Finally, by utilizing the attribution method of integrated gradients, we show
that the policy relies on a non-trivial combination of spatially and temporally
derived gradient information in a ratio that varies dynamically during the
chemotactic trajectories.

本研究通过深度强化学习方法，研究了在化学趋向作用中由空间梯度估计和时间梯度估计驱动的边界，并发现混合化学趋向策略在小细胞和大细胞之间的过渡是连续的，并且在过渡区域，混合化学趋向策略优于具有约束性的变体以及明确整合空间和时间信息的模型。

学习噪声趋化中空间和时间信息的最优集成

Learning optimal integration of spatial and temporal information in  noisy chemotaxis

In this paper, we explore deep learning methods for estimating when objects
were made. Automatic methods for this task could potentially be useful for
historians, collectors, or any individual interested in estimating when their
artifact was created. Direct applications include large-scale data organization
or retrieval. Toward this goal, we utilize features from existing deep networks
and also fine-tune new networks for temporal estimation. In addition, we create
two new datasets of 67,771 dated clothing items from Flickr and museum
collections. Our method outperforms both a color-based baseline and previous
state of the art methods for temporal estimation. We also provide several
analyses of what our networks have learned, and demonstrate applications to
identifying temporal inspiration in fashion collections.

本文提出了基于深度学习的物品制作时间估计方法，利用已有的深度网络特征和新网络 fine-tune 的方法进行训练，创建了两个包含 67,771 个衣物图像数据集并且通过实验和应用验证了其在物品时间信息识别和时尚风格计算方面的准确性和性能。