English(EN) Oscillatory Phase Coding Emerges in Adaptive RNNs for Flexible Working Memory

自适应循环神经网络为灵活工作记忆开发振荡相位编码

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了自适应循环神经网络（RNN），在对灵活工作记忆任务进行训练时，这些网络会自发地表现出振荡相位编码（OPC）。这种涌现的特性允许RNN通过让记忆的项目在共享节律的不同相位上重复出现来存储和处理信息，其中幅度编码项目身份，相位编码序列顺序。该研究确定了适应性、可变延迟和灵活读出对于OPC至关重要，从而导致网络中出现分别处理身份和序列的内容细胞和时间细胞。 AI

影响揭示了AI模型中灵活工作记忆的新机制，可能为认知任务的新架构带来启发。

排序理由详细介绍RNN中新计算机制的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Kehan Lu, Tianhao Chu, Aiersi Tuerhong, Yiwen Sun, Huan Luo, Fang Fang, Si Wu · 2026-11-06 05:00

自适应循环神经网络中出现振荡相位编码以实现灵活工作记忆

arXiv:2611.06932v1 Announce Type: cross Abstract: Oscillatory phase coding (OPC) is widely observed in neural systems and has been implicated in cognitive functions such as memory and sequence processing. However, its computational role and neural substrates remain largely unclea…

本文如何被排名

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自适应循环神经网络中出现振荡相位编码以实现灵活工作记忆

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