Liu Chenghao, Jia Shuncheng, Liu Hongxing, Zhao Xuanle, Li Chengyu T, Xu Bo, Zhang Tielin
Institute of Automation, Chinese Academy of Sciences, Beijing, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Commun Biol. 2025 Jan 28;8(1):137. doi: 10.1038/s42003-024-07282-3.
Whether working memory (WM) is encoded by persistent activity using attractors or by dynamic activity using transient trajectories has been debated for decades in both experimental and modeling studies, and a consensus has not been reached. Even though many recurrent neural networks (RNNs) have been proposed to simulate WM, most networks are designed to match respective experimental observations and show either transient or persistent activities. Those few which consider networks with both activity patterns have not attempted to directly compare their memory capabilities. In this study, we build transient-trajectory-based RNNs (TRNNs) and compare them to vanilla RNNs with more persistent activities. The TRNN incorporates biologically plausible modifications, including self-inhibition, sparse connection and hierarchical topology. Besides activity patterns resembling animal recordings and retained versatility to variable encoding time, TRNNs show better performance in delayed choice and spatial memory reinforcement learning tasks. Therefore, this study provides evidence supporting the transient activity theory to explain the WM mechanism from the model designing point of view.
工作记忆（WM）是通过使用吸引子的持续活动进行编码，还是通过使用瞬态轨迹的动态活动进行编码，这在实验和建模研究中已经争论了几十年，尚未达成共识。尽管已经提出了许多循环神经网络（RNN）来模拟工作记忆，但大多数网络都是为了匹配各自的实验观察结果而设计的，表现出瞬态或持续活动。少数考虑具有两种活动模式的网络尚未尝试直接比较它们的记忆能力。在本研究中，我们构建了基于瞬态轨迹的RNN（TRNN），并将它们与具有更持续活动的普通RNN进行比较。TRNN纳入了生物学上合理的修改，包括自我抑制、稀疏连接和分层拓扑。除了类似于动物记录的活动模式以及对可变编码时间保持通用性外，TRNN在延迟选择和空间记忆强化学习任务中表现出更好的性能。因此，本研究提供了证据，从模型设计的角度支持瞬态活动理论来解释工作记忆机制。
