The brain is targeted for processing temporal sequence information. It
remains largely unclear how the brain learns to store and retrieve sequence
memories. Here, we study how recurrent networks of binary neurons learn
sequence attractors to store predefined pattern sequences and retrieve them
robustly. We show that to store arbitrary pattern sequences, it is necessary
for the network to include hidden neurons even though their role in displaying
sequence memories is indirect. We develop a local learning algorithm to learn
sequence attractors in the networks with hidden neurons. The algorithm is
proven to converge and lead to sequence attractors. We demonstrate that the
network model can store and retrieve sequences robustly on synthetic and
real-world datasets. We hope that this study provides new insights in
understanding sequence memory and temporal information processing in the brain.

大脑针对处理时间序列信息，如何学习存储和检索序列记忆仍不清楚。本研究探讨了二元神经元回归网络如何学习序列吸引子以存储预定义的模式序列，并能够强大地检索它们。通过发展一种本地学习算法，证明了该算法能够使带有隐藏神经元的网络学习序列吸引子，且能够在合成和实际数据集上有效地存储和检索序列。希望本研究为理解大脑中的序列记忆和时间信息处理提供新的见解。

在具有隐藏神经元的循环网络中学习序列吸引子

Learning Sequence Attractors in Recurrent Networks with Hidden Neurons

Visual place recognition is particularly challenging when places suffer
changes in its appearance. Such changes are indeed common, e.g., due to
weather, night/day or seasons. In this paper we leverage on recent research
using deep networks, and explore how they can be improved by exploiting the
temporal sequence information. Specifically, we propose 3 different
alternatives (Descriptor Grouping, Fusion and Recurrent Descriptors) for deep
networks to use several frames of a sequence. We show that our approaches
produce more compact and best performing descriptors than single- and
multi-view baselines in the literature in two public databases.

本文使用深度神经网络，并提出 3 种不同的方法，即描述符组合、融合和循环描述符，探讨如何利用时间序列信息来改善视觉地点识别的困难。研究结果表明，在两个公共数据库中，我们的方法比单视角和多视角基线产生更简洁且一致的描述符，进而改善了视觉地点识别的准确性。