一个生物灵感的视觉触觉神经元,用于多感官集成
Muhtasim Ul Karim Sadaf1, Najam U Sakib1, Andrew Pannone1
1Engineering Science and Mechanics, Penn State University, University Park, PA, 16802, USA.
Nature communications
|September 15, 2023
概括
研究人员开发了一种人工视觉触觉神经元,模仿大脑的多感官集成. 这种由生物神经元启发的新型设备增强了人工智能能力,以更快,更准确地处理综合视觉和触觉信息.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 与处理单个感官输入相比,大脑中的多感官集成允许增强和更快的响应,特别是当单个线索较弱时.
- 从多种感官模式接收输入的专门神经元是多感官集成的生物基础.
- 目前的神经形态计算主要集中在单感官信息处理上,在模拟复杂的生物智能方面造成了差距.
研究的目的:
- 引入一种新的人工视觉触觉神经元,能够模拟生物多感官集成的关键特征.
- 开发一种固态设备,通过先进的神经形态计算来弥合人工智能和自然智能之间的差距.
- 展示一种以生物为灵感的方法来处理综合视觉和触觉信息.
主要方法:
- 整合光敏单层二硫化物 (MoS2) 记忆晶体管与 triboelectric 触觉传感器,以创建一个人工视觉触觉神经元.
- 人工神经元的表征,以捕捉基本的多感官集成特征:超添加反应,反向有效性和时间一致性.
- 开发一个编码集成视觉触觉信息到数字尖端事件的电路.
主要成果:
- 人工视觉触觉神经元成功模拟了在生物多感官神经元中观察到的超添加反应,反向有效性效应和时间一致性.
- 实现了一个功能电路,将视觉和触觉线索组合编码为数字尖峰输出,尖峰概率由线索强度调节.
- 该设备向生物启发的神经形态计算迈出了重要的一步,能够进行复杂的感官数据融合.
结论:
- 开发的人工视觉触觉神经元和尖端编码电路代表了神经形态计算的重大进步.
- 这项工作为更复杂的人工智能系统铺平了道路,这些系统可以处理和整合来自多个感官的信息,模仿自然智能.
- 这项研究强调了固态设备在模拟复杂的神经功能的潜力,用于未来的计算范式.
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