Extinction Learning from a Mechanistic and Systems Perspective

• Main Author: Onur Gunturkun (auth)
• Other Authors: Oliver T. Wolf (auth), Denise Manahan-Vaughan (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: Frontiers Media SA 2016
• Series: Frontiers Research Topics
• Subjects: Neurosciences; Extinction learning; Pavlovian conditioning; appetitive learning; renewal; predictive learning; neurotransmitter; brain structure; Fear conditioning
• Online Access: DOAB: download the publication; DOAB: description of the publication

Throughout their lifetime, animals learn to associate stimuli with their consequences. Following memory acquisition and consolidation, circumstances may arise that necessitate that initially learned behaviour is no longer relevant. The ensuing process is called extinction learning and involves a novel and complex learning procedure that involves a large number of neural entities. While the neural fundaments of the initial acquisition are well studied, our understanding of the behavioural and neural basis of extinction is still limited and derives mostly from rodent data acquired through fear conditioning paradigms. Fear conditioning and extinction in rodents is a spectacularly successful paradigm within behavioral neuroscience. However, in recent years, new approaches have been emerging that examine the mechanisms of extinction learning in different setting that also involve appetitive models, a broader comparative perspective, a focus on other brain systems, an examination of hormonal factors, and conditioning of immune responses. Only a broader analysis of the neural fundaments of extinction learning will finally uncover shared and distinct mechanisms that underlie extinction learning in different functional systems. The papers compiled in this Research Topic offer new and valuable insights into the mechanisms and functional implementation of extinction learning at its different levels of complexity, and form the basis for new concepts and research ideas in this field.