Dynamic tuning of neural stability for cognitive control

• 成果类型: Article
• 署名作者: Xu, Muyuan; Hosokawa, Takayuki; Tsutsui, Ken- Ichiro; Aihara, Kazuyuki
• 署名单位: University of Tokyo; Tohoku University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10628
• DOI: 10.1073/pnas.2409487121
• 发表日期: 2024-12-03
• 关键词: prefrontal cortex; transient dynamics; DECISION; category; REPRESENTATION; connectionism; computations; architecture; networks; systems

摘要

The brain is thought to execute cognitive control by actively maintaining and flexibly updating patterns of neural activity that represent goals and rules. However, while actively maintaining patterns of activity requires robustness against noise and distractors, updating the activity requires sensitivity to task-- relevant inputs. How these conflicting demands can be reconciled in a single neural system remains unclear. Here, we study the prefrontal cortex of monkeys maintaining a covert rule and integrating sensory inputs toward a choice. Following the onset of neural responses, sensory integration evolves with a 70 ms delay. Using a stability analysis and a recurrent neural network model trained to perform the task, we show that this delay enables a transient, system- level destabilization, opening a temporal window to selectively incorporate new information. This mecha- nism allows robustness and sensitivity to coexist in a neural system and hierarchically updates patterns of neural activity, providing a general framework for cognitive control. Furthermore, it reveals a learned, explicit rule representation, suggesting a reconciliation between the symbolic and connectionist approaches for building intelligent machines.