论文标题
复发性神经网络中的短期记忆通过瞬态振荡动力学
Short term memory by transient oscillatory dynamics in recurrent neural networks
论文作者
论文摘要
尽管短期记忆在认知功能中具有重要意义，但编码和维持神经活动动态中输入信息的过程仍然难以捉摸。本文中，我们揭示了瞬时神经动力学对短期记忆的重要性。通过训练复发性神经网络以短期记忆任务并分析动力学，获得了短期记忆机制的特征，其中在瞬态振荡的幅度中编码了输入信息，而不是固定的神经活动。这种瞬态轨迹被缓慢的流形吸引，从而允许丢弃无关的信息。此外，我们研究了动力学对噪声的鲁棒性的过程。在这种短暂的振荡中，通过扰动到歧管后神经状态的强收缩获得了对噪声的鲁棒性。该机制适用于多种神经网络模型和任务，这意味着它与一般的神经信息处理相关。
Despite the significance of short-term memory in cognitive function, the process of encoding and sustaining the input information in neural activity dynamics remains elusive. Herein, we unveiled the significance of transient neural dynamics to short-term memory. By training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristics of the short-term memory mechanism were obtained in which the input information was encoded in the amplitude of transient oscillations, rather than the stationary neural activities. This transient trajectory was attracted to a slow manifold, which permitted the discarding of irrelevant information. Additionally, we investigated the process by which the dynamics acquire robustness to noise. In this transient oscillation, the robustness to noise was obtained by a strong contraction of the neural states after perturbation onto the manifold. This mechanism works for several neural network models and tasks, which implies its relevance to neural information processing in general.
