协同学习突触延迟,重量和适应在尖端神经网络中
Lucas Deckers1, Laurens Van Damme1, Werner Van Leekwijck1
1IDLab, imec, University of Antwerp, Antwerp, Belgium.
Frontiers in neuroscience
|April 29, 2024
概括
尖端神经网络 (SNN) 通过共同学习,通过神经适应和突触延迟来改善语音识别. 这种生物启发的方法增强了时间处理,超过了传统的人工神经网络 (ANN).
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 神经形态工程的神经形态工程
背景情况:
- 尖端神经网络 (SNN) 为神经形态硬件提供了比人工神经网络 (ANN) 更高效的时间处理优势.
- 通过生物灵感特征来增强SNN,可以提高它们在复杂任务上的性能.
研究的目的:
- 研究协同学习突触权重对神经元适应和SNN突触传播延迟的影响.
- 评估这些增强型SNN与基线SNN和ANN在语音识别任务中的性能.
主要方法:
- 协同学习突触权重与神经元适应参数,使神经元能够从过去的活动中学习.
- 协同学习突触重量与突触传播延迟以关联暂时遥远的尖峰列车.
- 使用简单的2个隐藏层的前网络架构.
主要成果:
- 神经元适应和突触延迟都单独改善了SNN的表现.
- 这两种特征的结合在研究的语音识别数据集上取得了最先进的结果.
- 增强的SNN在神经形态和大规模语音数据集上表现优于基准ANN,包括GRU,即使参数较少.
结论:
- 共同学习的生物灵感特征显著提高了SNN对时间处理的能力.
- 这些由大脑启发的改进允许SNN在具有丰富时间动态的任务中超越同等ANN.
- 这项研究表明了开发更高效和更强大的神经形态系统的有希望的方向.
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