学习和推断与相关的神经变异性相关
Yang Qi1,2,3, Zhichao Zhu1,2, Yiming Wei1,4
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.
PNAS nexus
|October 13, 2025
概括
随机神经计算 (SNC) 理论使基于梯度的学习能够在尖端神经网络 (SNN) 中实现,尽管存在噪音. 这种方法提高了推断速度,并通过优化发射速率和相关性来创建生物学上可信的模型.
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
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 大脑内在的噪音表明,随机性对于神经计算至关重要.
- 在相关噪音下的尖端神经网络 (SNN) 中的学习仍然是一个挑战.
研究的目的:
- 开发一个基于梯度的学习理论在SNN在噪音驱动的制度.
- 为SNN引入一种新的深度学习架构.
主要方法:
- 拟议的随机神经计算 (SNC) 理论使用时刻闭合方法.
- 引入时刻神经网络 (MNN),将基于速率的网络泛化为二次时刻.
- 从MNN转移到SNN的直接参数转移,没有微调.
主要成果:
- 训练有素的MNN捕捉到现实的生物神经元发射统计数据 (速率分布,Fano因子,弱相关性).
- 优化的平均发射速率和相关性结构提高了任务准确性,减少了预测不确定性.
- 通过联合操纵射击速度和相关性,实现了增强的推断速度.
结论:
- SNC框架为SNN不确定性处理提供了洞察力.
- 能够构建具有相关可变性的生物可信的神经电路模型.
- 在英特尔的Loihi神经形态硬件上展示了实际应用.
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