This paper develops an innovative method that enables neural networks to
generate and utilize knowledge graphs, which describe their concept-level
knowledge and optimize network parameters through alignment with human-provided
knowledge. This research addresses a gap where traditionally, network-generated
knowledge has been limited to applications in downstream symbolic analysis or
enhancing network transparency. By integrating a novel autoencoder design with
the Vector Symbolic Architecture (VSA), we have introduced auxiliary tasks that
support end-to-end training. Our approach eschews traditional dependencies on
ontologies or word embedding models, mining concepts from neural networks and
directly aligning them with human knowledge. Experiments show that our method
consistently captures network-generated concepts that align closely with human
knowledge and can even uncover new, useful concepts not previously identified
by humans. This plug-and-play strategy not only enhances the interpretability
of neural networks but also facilitates the integration of symbolic logical
reasoning within these systems.

本研究开发了一种创新方法，使神经网络能够生成和利用知识图谱，通过与人类提供的知识对齐，描述概念级知识并优化网络参数。实验结果表明，我们的方法始终捕捉到与人类知识密切对齐的网络生成的概念，并且甚至能够发现人类之前未发现的新概念。这种策略不仅增强了神经网络的可解释性，而且有助于在这些系统中集成符号逻辑推理。

将人类和由神经网络生成的知识图进行特定任务对齐

Aligning Knowledge Graphs Provided by Humans and Generated from Neural  Networks in Specific Tasks

Autonomous agents require self-localization to navigate in unknown
environments. They can use Visual Odometry (VO) to estimate self-motion and
localize themselves using visual sensors. This motion-estimation strategy is
not compromised by drift as inertial sensors or slippage as wheel encoders.
However, VO with conventional cameras is computationally demanding, limiting
its application in systems with strict low-latency, -memory, and -energy
requirements. Using event-based cameras and neuromorphic computing hardware
offers a promising low-power solution to the VO problem. However, conventional
algorithms for VO are not readily convertible to neuromorphic hardware. In this
work, we present a VO algorithm built entirely of neuronal building blocks
suitable for neuromorphic implementation. The building blocks are groups of
neurons representing vectors in the computational framework of Vector Symbolic
Architecture (VSA) which was proposed as an abstraction layer to program
neuromorphic hardware. The VO network we propose generates and stores a working
memory of the presented visual environment. It updates this working memory
while at the same time estimating the changing location and orientation of the
camera. We demonstrate how VSA can be leveraged as a computing paradigm for
neuromorphic robotics. Moreover, our results represent an important step
towards using neuromorphic computing hardware for fast and power-efficient VO
and the related task of simultaneous localization and mapping (SLAM). We
validate this approach experimentally in a simple robotic task and with an
event-based dataset, demonstrating state-of-the-art performance in these
settings.

本文提出一种利用神经学中向量符号体系（VSA）的计算范式构建的适用于神经形态计算的视觉里程计（VO）算法。该 VO 网络不仅可以生成并存储所呈现视觉环境的工作内存，同时在估计摄像机的位置和方向变化的同时更新这个工作内存。该文实验验证了该方法在机器人的简单任务和基于事件的数据集上关于 SLAM 任务的高性能表现。

共振网络的神经形态视觉里程计

Neuromorphic Visual Odometry with Resonator Networks

Logics with analogous semantics, such as Fuzzy Logic, have a number of
explanatory and application advantages, the most well-known being the ability
to help experts develop control systems. From a cognitive systems perspective,
such languages also have the advantage of being grounded in perception. For
social decision making in humans, it is vital that logical conclusions about
others (cognitive empathy) are grounded in empathic emotion (affective
empathy). Classical Fuzzy Logic, however, has several disadvantages: it is not
obvious how complex formulae, e.g., the description of events in a text, can be
(a) formed, (b) grounded, and (c) used in logical reasoning. The two-layered
Context Logic (CL) was designed to address these issue. Formally based on a
lattice semantics, like classical Fuzzy Logic, CL also features an analogous
semantics for complex fomulae. With the Activation Bit Vector Machine (ABVM),
it has a simple and classical logical reasoning mechanism with an inherent
imagery process based on the Vector Symbolic Architecture (VSA) model of
distributed neuronal processing. This paper adds to the existing theory how
scales, as necessary for adjective and verb semantics can be handled by the
system.

通过 Context Logic 和 Activation Bit Vector Machine，可以解决 Fuzzy Logic 对复杂公式的处理不足，VSA 模型则提供了基于向量符号体系结构的内在图像处理机制，本文研究了如何处理形容词和动词语义的等级。