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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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基于自适应性STDP的芯片上尖峰模式检测.

Ashish Gautam1, Takashi Kohno1

  • 1Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.

Frontiers in neuroscience
|July 31, 2023
PubMed
概括

适应性STDP是一种生物启发的学习规则,尽管突触功效分辨率低,但在神经形态芯片上保持了性能. 这种无监督学习方法可以有效地检测硬件中的噪音尖峰模式.

科学领域:

  • 神经形态工程的神经形态工程
  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能

背景情况:

  • 尖端神经网络 (SNN) 对于模拟大脑微电路至关重要,是关键的神经形态计算模型.
  • 峰值时间依赖可塑性 (STDP) 是SNN中常见的无监督学习规则,但由于有限的突触效率分辨率 (通常≤6位),其性能在神经形态硬件上下降.
  • 模拟通常使用64位浮点精度,与硬件实现创建性能差距.

研究的目的:

  • 实验验证在神经形态芯片上适应性STDP (一种生物启发的学习规则) 的性能.
  • 证明适应性STDP可以实现模拟级性能,具有低分辨率的突触效率 (4位固定点).
  • 在混合信号CMOS神经形芯片上首次成功实现了无监督的杂的时空尖峰模式检测,该芯片保持了模拟性能.

主要方法:

  • 开发并模拟了自适应性STDP,一种使用4位固定点突触功效的生物灵感学习规则.
  • 在使用TSMC 250nm技术制造的定制混合信号CMOS神经形芯片上实现了自适应性STDP学习.
  • 该芯片集成了 soma 和 256 个突触电路与学习电路.

主要成果:

  • 实验结果证实,自适应性STDP学习的性能与使用高精度模拟的传统STDP学习相提并论.
  • 神经形芯片成功地展示了无人监督的杂的时空尖峰模式检测,具有低分辨率的突触功效.
关键词:
4位的突触突触是4位的突触.适应性的STDP是可以适应的.混合信号的神经形态芯片尖的模式检测和检测.刺激神经网络的神经网络.突触解决方案 突触解决方案时间编码时间编码.没有监督的学习学习.

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  • 该研究实现了显著减少突触效率的解决方案,而不会影响学习表现.

    • 结论:

    • 适应性STDP有效地弥合了SNN模拟和神经形态硬件实现之间的性能差距.
    • 这项工作首次实验展示了在神经形态芯片上具有有限精度的高性能无监督噪音尖峰模式检测.
    • 这些发现为利用生物启发的学习规则开发更高效,更可扩展的神经形态系统铺平了道路.