A neural-network model of the dynamics of hunger, learning, and action vigor in mice

Recently the computational-neuroscience literature on animals' learning has
proposed some models for studying organisms' decisions related to the energy
to invest in the execution of actions (\vigor"). These models are based on
average reinforcement learning algorithms which make it possible to reproduce
organisms' behaviours and at the same time to link them to speciØc brain
mechanisms such as phasic and tonic dopamine-based neuromodulation. This
paper extends these models by explicitly introducing the dynamics of hunger,
driven by energy consumption and food ingestion, and the eÆects of hunger on
perceived reward and, consequently, vigor. The extended model is validated by
addressing some experiments carried out with real mice in which reinforcement
schedules delivering lower amounts of food can lead to a higher vigor compared
to schedules delivering larger amounts of food due to the higher perceived
reward caused by higher levels of hunger.