We describe a neural network model of the mouse amygdala and hypothalamus that is able to support competitive interactions between social and grooming behaviors, as well as basic findings about how these behaviors are modulated by light stimulation. The model is built on attractor network dynamics and involves mutual inhibition among different populations of neurons. Simulation results are found to be consistent with some experimental observations such as scalable control of social behaviors and opponent control of social and nonsocial behaviors. Bifurcation analysis is applied in order to understand the dynamical basis of state transitions and to show the dynamics emergent from the model. We conclude that this study also provides some mechanistic insights of behavioral state transitions.