Concept learning is a fundamental aspect of human cognition and plays a
critical role in mental processes such as categorization, reasoning, memory,
and decision-making. Researchers across various disciplines have shown
consistent interest in the process of concept acquisition in individuals. To
elucidate the mechanisms involved in human concept learning, this study
examines the findings from computational neuroscience and cognitive psychology.
These findings indicate that the brain's representation of concepts relies on
two essential components: multisensory representation and text-derived
representation. These two types of representations are coordinated by a
semantic control system, ultimately leading to the acquisition of concepts.
Drawing inspiration from this mechanism, the study develops a human-like
computational model for concept learning based on spiking neural networks. By
effectively addressing the challenges posed by diverse sources and imbalanced
dimensionality of the two forms of concept representations, the study
successfully attains human-like concept representations. Tests involving
similar concepts demonstrate that our model, which mimics the way humans learn
concepts, yields representations that closely align with human cognition.

概念学习是人类认知的基本方面，与分类、推理、记忆和决策等心理过程密切相关。通过研究计算神经科学和认知心理学的发现，本文发现大脑对概念的表示依赖于多感官表示和文本派生表示两个关键组成部分，并通过语义控制系统协调这两种表示，最终实现概念的获取。借鉴这一机制，本研究基于脉冲神经网络开发了一种人类类似的概念学习计算模型，有效应对多样化来源和不平衡维度的概念表示所带来的挑战，并成功获得了类似人类认知的概念表示。类似概念的测试表明，我们的模型模拟了人类学习概念的方式，得到了与人类认知密切吻合的表示结果。

基于脑灵感的计算模型用于类人概念学习

A Brain-inspired Computational Model for Human-like Concept Learning

The thud of a bouncing ball, the onset of speech as lips open -- when visual
and audio events occur together, it suggests that there might be a common,
underlying event that produced both signals. In this paper, we argue that the
visual and audio components of a video signal should be modeled jointly using a
fused multisensory representation. We propose to learn such a representation in
a self-supervised way, by training a neural network to predict whether video
frames and audio are temporally aligned. We use this learned representation for
three applications: (a) sound source localization, i.e. visualizing the source
of sound in a video; (b) audio-visual action recognition; and (c) on/off-screen
audio source separation, e.g. removing the off-screen translator's voice from a
foreign official's speech. Code, models, and video results are available on our
webpage: this http URL

本文提出了一种融合多感官表征的方法，通过神经网络自动预测视频帧和音频的时间对齐情况，实现声音定位、视听行为识别和音频源分离等三个应用。