通过脑启发的E-I平衡的自适应控制来增强储水库计算
Keshav Srinivasan1,2, Dietmar Plenz1, Michelle Girvan3,4,5
1Section on Critical Brain Dynamics, National Institute of Mental Health, Bethesda, MD, USA.
Nature communications
|November 20, 2025
概括
在容器计算机 (RC) 中调整激发-抑制 (E-I) 平衡可显著提高性能. 一种新的自我适应机制优化了这种平衡,增强了记忆和预测任务.
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
- 机器学习 机器学习
背景情况:
- 储库计算机 (RCs) 是由大脑原理启发的循环神经网络,为深度学习提供了有效的替代方案.
- RC通常使用固定的内部连接和训练的输出权重,但对超参数敏感.
- 刺激-抑制 (E-I) 信号平衡对大脑功能至关重要,通常固定在RC中,尽管它对表现有影响.
研究的目的:
- 调查调整RC中E-I信号平衡的影响.
- 引入和评估一个自我调整的机制,以调整 E-I 余额.
- 探索改善RC性能和减少超参数调的潜力.
主要方法:
- 系统地调查RCs中不同的E-I平衡比率.
- 开发和实施一种自适应机制,以根据目标火速调整本地E-I平衡.
- 使用内存容量和时间序列预测任务进行性能评估,包括使用异质火速目标进行实验.
主要成果:
- 在平衡或略过抑制的E-I疗法中始终观察到最佳RC性能,而不是激发主导的疗法.
- 自适应机制显著降低了超参数调整成本.
- 在内存容量和时间序列预测任务中实现了高达130%的性能增长.
- 将异质性纳入射速目标进一步提高了稳定性.
结论:
- 动态适应E-I平衡是提高RC性能的一个关键设计原则.
- 拟议的自适应机制提供了一种实用的方法来优化RC和减少调整复杂性.
- 研究结果为神经计算提供了洞察力,并建议开发更高效和更强大的人工神经网络的途径.
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