Detection of latent brain states from spontaneous neural activity in the amygdala - Aucoin, Alexa; Lin, Kevin K.; Gothard, Katalin M. (PLOS Computational Biology, 2025)
Author summary The amygdala responds to a large array of socially and emotionally salient stimuli, both external (e.g., affective or neutral touch) and internal (e.g., heart rate), and the context in which these stimuli are processed. It thus plays an important role in how social animals like primates interact. Recent work showed that individual cells in the amygdala carry contextual information during the periods between stimuli, characterized by spontaneous baseline activity, i.e., socially significant interactions lead to detectable, persistent changes in activity in the amygdala. Here, we show that using modern machine learning techniques, we can identify, during this period of spontaneous activity, context-related signals not only from the individual neurons but also from the collective activity of large groups of brain cells. As a central task of computational neuroscience is to identify cognitive states and their neural and behavioral correlates, this study hints at how contextual information is encoded in the amygdala and contributes to the growing understanding of the potentials and pitfalls of machine learning methods in neuroscience.