English(EN) Random matrix theory of sparse neuronal networks with heterogeneous timescales

随机矩阵理论应用于具有异质时间尺度的稀疏神经网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种随机矩阵系综，用于分析具有异质时间尺度的稀疏神经网络的雅可比矩阵。该系综能够准确捕捉训练过的雅可比矩阵的谱，其特征是特定的抑制性核心-兴奋性外围结构。该研究利用统计场论和超对称方法对谱边缘进行解析描述，将稀疏性和权重方差等网络参数与稳健工作记忆计算的关键特征联系起来。 AI

影响为理解复杂神经网络动力学提供了理论见解，可能为未来AI架构提供信息。

排序理由学术论文发表在arXiv上，详细介绍了理解神经网络的理论进展。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Thiparat Chotibut, Oleg Evnin, Weerawit Horinouchi · 2026-09-15 04:00

异质时间尺度稀疏神经网络的随机矩阵理论

arXiv:2512.12767v2 Announce Type: replace-cross Abstract: Training recurrent neuronal networks consisting of excitatory (E) and inhibitory (I) units with additive noise for working memory computation slows and diversifies inhibitory timescales, leading to improved task performanc…