Rungratsameetaweemana Nuttida, Kim Robert, Chotibut Thiparat, Sejnowski Terrence J
Department of Biomedical Engineering, Columbia University, New York, NY 10027.
Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037.
Proc Natl Acad Sci U S A. 2025 Jan 21;122(3):e2316745122. doi: 10.1073/pnas.2316745122. Epub 2025 Jan 16.
Recurrent neural networks (RNNs) based on model neurons that communicate via continuous signals have been widely used to study how cortical neural circuits perform cognitive tasks. Training such networks to perform tasks that require information maintenance over a brief period (i.e., working memory tasks) remains a challenge. Inspired by the robust information maintenance observed in higher cortical areas such as the prefrontal cortex, despite substantial inherent noise, we investigated the effects of random noise on RNNs across different cognitive functions, including working memory. Our findings reveal that random noise not only speeds up training but also enhances the stability and performance of RNNs on working memory tasks. Importantly, this robust working memory performance induced by random noise during training is attributed to an increase in synaptic decay time constants of inhibitory units, resulting in slower decay of stimulus-specific activity critical for memory maintenance. Our study reveals the critical role of noise in shaping neural dynamics and cognitive functions, suggesting that inherent variability may be a fundamental feature driving the specialization of inhibitory neurons to support stable information processing in higher cortical regions.
基于通过连续信号进行通信的模型神经元的循环神经网络（RNN）已被广泛用于研究皮质神经回路如何执行认知任务。训练此类网络以执行需要在短时间内保持信息的任务（即工作记忆任务）仍然是一项挑战。受前额叶皮质等高级皮质区域中观察到的强大信息维持能力的启发，尽管存在大量内在噪声，我们研究了随机噪声对跨不同认知功能（包括工作记忆）的RNN的影响。我们的研究结果表明，随机噪声不仅加快了训练速度，还增强了RNN在工作记忆任务上的稳定性和性能。重要的是，训练期间由随机噪声诱导的这种强大的工作记忆性能归因于抑制性单元的突触衰减时间常数增加，导致对记忆维持至关重要的刺激特异性活动的衰减减慢。我们的研究揭示了噪声在塑造神经动力学和认知功能中的关键作用，表明内在变异性可能是驱动抑制性神经元专业化以支持高级皮质区域中稳定信息处理的一个基本特征。
