让机器人用类脑方式实现听觉视觉融合感知
Spiking neural networks: Towards bio-inspired multimodal perception in robotics
- 构建类脑多模态感知模型，融合声音与视觉信号
- 提升机器人在真实场景中的人机交互生物合理性
- 适合关注类脑计算与人机交互的研究者
脉冲神经网络（SNNs）近年来受到广泛关注，源于神经科学并逐步进入人工智能领域。然而，由于其本质特性，SNNs 在性能上仍远落后于深度神经网络（DNNs）。为提升性能，许多研究尝试借鉴 DNN 的学习方法，取得一定进展。本文提出新视角：通过增强模型的生物合理性，以充分释放 SNNs 的潜力。我们设计了一种类脑的视听信号联合处理机制，用于识别任务，旨在实现更符合生物特性的智能机器人人机交互应用。
原文摘要 · Abstract (English)
Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications.
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