在包含抑制后反弹激发的神经形态电路中持续的尖端活动
Archita Hore1, Sharba Bandyopadhyay1, Saswat Chakrabarti1
1Indian Institute of Technology Kharagpur, India.
Journal of neural engineering
|June 11, 2024
概括
这项研究将抑制后反弹刺激 (PIRE) 集成到硬件神经回路中,使认知功能必不可少的持续活动成为可能. 节能的神经形态模型展示了先进的人工智能应用的持续神经发射.
科学领域:
- 神经科学是一个神经科学.
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 持续的活动对于记忆和注意力等认知功能至关重要.
- 神经形态系统需要高效的硬件实现先进的人工智能.
- 整合生物神经现象可以增强神经形态电路的能力.
研究的目的:
- 引入一种新的方法,将抑制后反弹刺激 (PIRE) 集成到神经回路中.
- 通过使用PIRE证明在神经网络中持续活动的产生.
- 设计和模拟基于PIRE的节能神经形态电路.
主要方法:
- 在Cadence Virtuoso中设计和模拟神经元和突触电路,使用TSMC 180nm技术.
- 将PIRE现象集成到硬件神经元模型中.
- 调整了电路以模仿生物神经元反应,并评估了功率/能源效率.
主要成果:
- 在两个神经元网络中,在强大的抑制输入下证明了PIRE的发生.
- 实现了神经尖端持续触发长达1.7秒的时间.
- 通过平均功耗消耗和每峰值的能量消耗来评估电路效率.
结论:
- 集成到 PIRE 的模型能够在硬件中持续生成活动,这对于神经形态系统至关重要.
- 这种方法促进了认知功能的硬件实现.
- 节能设计适用于各种AI应用,包括物联网和脑计算机接口.
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