Recent human pharmacological fMRI studies suggest that oxytocin (OT) is a centrally-acting neurotransmitter important in the development and expression of trusting relationships in men and women. OT administration in humans was shown to increase trust, acceptance of social risk, memory of faces, and inference of the emotional state of others, in part by directly inhibiting the amygdala. However, the cerebral microcircuitry underlying this mechanism is still unclear. Here, we propose a spiking integrate-and-fire neuronal model of several key interacting brain regions affected by OT neurophysiology during social trust behavior. As a social behavior scenario, we embodied the brain simulator in a behaving virtual humanoid neurorobot, which interacted with a human via a camera. At the physiological level, the amygdala tonic firing was modeled using our recurrent asynchronous irregular nonlinear (RAIN) network architecture. OT cells were modeled with triple apical dendrites characteristic of their structure in the paraventricular nucleus of the hypothalamus. Our architecture demonstrated the success of our system in learning trust by discriminating concordant from discordant movements of a human actor. This led to a cooperative versus protective behavior by the neurorobot after being challenged by a new intent.

The destructive capacity of wildfires and the dangers and limitations associated with observing actual wildfires has led researchers to develop mathematical models in order to better understand their behavior; unfortunately, current two-dimensional visualization techniques make discerning driving forces of a fire difficult and restrict comprehension to the trained eye. VFire is an immersive wildfire visualization application that aims to ameliorate these problems. We discuss several recent enhancements made to VFire in order to not only increase the amount of meaningfully visualized data and visual fidelity but also improve user interaction.