标题： 脑内吸引子动力学模型 显示英文标题

标题： Models of attractor dynamics in the brain

摘要： 吸引子动力学是神经回路中的基本计算主题，通过稳定的、自我维持的神经活动模式支持各种认知功能。在这些讲义笔记中，我们回顾了四个关键例子，展示了自联想神经网络模型如何阐明在执行认知功能的生物神经系统的基于吸引子的信息处理的计算机制。根据实验证据，我们探讨了海马空间表示、下颞皮层的视觉分类、知觉适应和启动以及由感觉历史塑造的工作记忆偏差。在这些领域中，吸引子网络模型揭示了共同的计算原理，并提供了分析性见解，说明经验如何塑造神经活动和行为。我们的综合强调了吸引子模型作为探究认知和行为神经基础的强大工具的价值。 显示英文摘要

摘要： Attractor dynamics are a fundamental computational motif in neural circuits, supporting diverse cognitive functions through stable, self-sustaining patterns of neural activity. In these lecture notes, we review four key examples that demonstrate how autoassociative neural network models can elucidate the computational mechanisms underlying attractor-based information processing in biological neural systems performing cognitive functions. Drawing on empirical evidence, we explore hippocampal spatial representations, visual classification in the inferotemporal cortex, perceptual adaptation and priming, and working-memory biases shaped by sensory history. Across these domains, attractor network models reveal common computational principles and provide analytical insights into how experience shapes neural activity and behavior. Our synthesis underscores the value of attractor models as powerful tools for probing the neural basis of cognition and behavior.