Learning predictive signals within a local recurrent circuit

• 成果类型: Article
• 署名作者: Asabuki, Toshitake; Gillon, Colleen J.; Clopath, Claudia
• 署名单位: Imperial College London; RIKEN
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14038
• DOI: 10.1073/pnas.2414674122
• 发表日期: 2025-07-08
• 关键词: mismatch negativity mmn; visual-cortex; fmri; erp

摘要

The predictive coding hypothesis proposes that top-down predictions are compared with incoming bottom-up sensory information, with prediction errors signaling the discrepancies between these inputs. While this hypothesis explains the presence of prediction errors, recent experimental studies suggest that prediction error signals can emerge within a local circuit, that is, from bottom-up sensory input alone. In this paper, we test whether local circuits alone can generate predictive signals by training a recurrent spiking network using local plasticity rules. Our network model replicates experimentally observed features of prediction errors, such as biphasic neural activity patterns and context dependency. Our findings shed light on how synaptic plasticity can shape prediction errors and enable the acquisition and updating of an internal model of sensory input within a recurrent neural network.