Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation

• 成果类型: Article
• 署名作者: Rungratsameetaweemana, Nuttida; Kim, Robert; Chotibut, Thiparat; Sejnowski, Terrence J.
• 署名单位: Columbia University; Salk Institute; Cedars Sinai Medical Center; Chulalongkorn University; University of California System; University of California San Diego; University of California System; University of California San Diego
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10607
• DOI: 10.1073/pnas.2316745122
• 发表日期: 2025-01-21
• 关键词: timescales; neurons; interneurons; INFORMATION; hierarchy; areas

摘要

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.