基于晶体管的光电子神经元与用于神经形态计算的挥发性值切换记忆元相结合
Yanmei Sun1, Xinru Meng1, Gexun Qin1
1School of Electronic Engineering, Heilongjiang University, Harbin 150080, China; Heilongjiang Provincial Key Laboratory of Micro-nano Sensitive Devices and Systems, Heilongjiang University, Harbin 150080, China.
Journal of colloid and interface science
|September 8, 2024
概括
研究人员开发了一种光电子神经元,模仿视网膜功能,用于先进的神经形态视觉硬件. 这种人工神经元复制视觉处理,显示出对图像识别和仿生系统的希望.
科学领域:
- 神经形态工程的神经形态工程
- 生物模拟视觉系统 生物模拟视觉系统
- 人工智能 硬件 硬件
背景情况:
- 人类视觉系统,特别是视网膜,对于预处理视觉信息至关重要,激发了神经形态视觉硬件的发展.
- 现有的神经形态方法旨在复制视网膜的神经生物学功能,以增强视觉处理能力.
研究的目的:
- 开发一种新的光电子神经元,模仿视网膜神经元的功能行为.
- 整合电气和光学调制以实现生物可信的神经元信号传输.
- 为了评估由这些人工神经元组成的尖端神经网络的性能,用于图像识别任务.
主要方法:
- 通过将门调节的PDVT-10通道与挥发性值切换记忆器相结合,制造一个光电子神经元.
- 实施电阻匹配机制以实现光电子性能.
- 使用开发的漏洞整合和发射神经元进行图像分类,模拟一个感知子尖端神经网络.
主要成果:
- 开发出来的光电子尖端神经元成功地改变了它的尖端行为,模仿视网膜处理.
- 人工神经元通过结合的电和光学调制证明了生物可信的信号传输.
- 神经元发射在黑暗中被抑制,在暴露于光线时被激活,复制了视网膜反应.
- 感知器尖端神经网络显示了对图像识别算法的分类能力.
结论:
- 这种新型的光电子神经元有效地复制了视网膜的关键功能,为仿生视觉提供了一个有前途的途径.
- 这项研究为开发用于神经形态硬件应用的可扩展和层次化的尖端电子器件铺平了道路.
- 这些发现有助于推进神经形态视觉领域的发展,在图像识别及其他领域有潜在的应用.
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