International Research Center for Neurointelligence, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
Department of Orthoptics, Faculty of Rehabilitation, Kawasaki University of Medical Welfare, Kurashiki, Okayama 701-0193, Japan.
Proc Natl Acad Sci U S A. 2024 Dec 3;121(49):e2409487121. doi: 10.1073/pnas.2409487121. Epub 2024 Nov 25.
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 mechanism 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.
大脑被认为通过主动维持和灵活更新代表目标和规则的神经活动模式来执行认知控制。然而，虽然主动维持活动模式需要对噪声和干扰有鲁棒性，但更新活动需要对与任务相关的输入敏感。在单个神经系统中如何协调这些相互冲突的需求尚不清楚。在这里，我们研究了猴子的前额叶皮层，它们在保持隐蔽规则的同时整合感官输入以做出选择。在神经反应开始后，感官整合会延迟 70 毫秒。我们使用稳定性分析和一个经过训练以执行任务的递归神经网络模型表明，这种延迟会导致系统级别的短暂不稳定性，从而打开一个时间窗口，以选择性地纳入新信息。这种机制允许在神经系统中同时存在鲁棒性和敏感性，并分层更新神经活动模式，为认知控制提供了一个通用框架。此外，它揭示了一种学习的、显式的规则表示，这表明在构建智能机器方面，符号和连接主义方法之间存在一种和解。
