Topic: Behavioral adaptation

During development, neural circuits are shaped continuously as we learn to control our bodies, with the goal of producing neural dynamics that enable the rich repertoire of behaviors we perform with our limbs. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories---supervised learning (SL) and reinforcement learning (RL)---that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of

Over the course of evolution, the amygdala (AMG) and medial frontal cortex (mPFC) network, involved in behavioral adaptation, underwent structural changes in the old-world monkey and human lineages. Yet, whether and how the functional organization of this network differs remains poorly understood. Using resting-state functional magnetic resonance imagery, we show that the functional connectivity (FC) between AMG nuclei and mPFC regions differs between humans and awake macaques. In humans, the AMG-mPFC FC displays U-shaped pattern along the corpus callosum: a positive FC with the ventromedial p