Social interactions, a ubiquitous aspect of our everyday life, are critical to the health and survival of the species, but little is known about their underlying neural computations. The major limitation preventing our understanding of the neural underpinnings of social cognition is the lack of a suitable framework to allow us to study how it emerges in real time from interactions among brain networks. Indeed, examining the neural bases of complex social interactions has been traditionally performed by studying the brain of nonhuman primates in a laboratory environment in which the head and body are restrained while synthetic stimuli are presented on a computer monitor. However, it has become increasingly understood that studying the brain in spatially confined, artificial laboratory rigs poses severe limits on our capacity to understand the function of brain circuits. To overcome these limitations, we propose a novel approach to understand the neural underpinnings of social cognition. We will use a high-yield wireless system to study the cortical dynamics and plasticity of social interactions by recording population activity in multiple visual, temporal, and prefrontal cortical areas while nonhuman primates are interacting freely with their environment and with other animals. This new approach will enable us to uncover the dynamics of neuronal network activity that drives social interactions in an ethologically relevant behavioral task that involves sensory integration, memory, and complex decision-making. Our integrative project brings together innovative brain recording technologies and microelectronics together with large data sets analysis techniques. Our proposed research will constitute a paradigm shift by moving social neuroscience ? from simply observing animal behavior and recording the responses of single cells ? to a quantitative understanding of the distributed neuronal network encoding during social behavior in freely moving nonhuman primates performing rich naturalistic tasks. We anticipate that the large quantity of neural data recorded using our approach will be of great interest to clinicians and computational neuroscientists studying general properties of normal and dysfunctional neural networks, possibly leading to medical insights into the mechanisms of autism and attention deficit disorders that impair social interactions.