Models of heterogeneous dopamine signaling in an insect learning and memory center

Abstract

The Drosophila mushroom body exhibits dopamine dependent synaptic plasticity that underlies the acquisition of associative memories. Recordings of dopamine neurons in this system have identified signals related to external reinforcement such as reward and punishment. However, other factors including locomotion, novelty, reward expectation, and internal state have also recently been shown to modulate dopamine neurons. This heterogeneity is at odds with typical modeling approaches in which these neurons are assumed to encode a global, scalar error signal. How is dopamine dependent plasticity coordinated in the presence of such heterogeneity? We develop a modeling approach that infers a pattern of dopamine activity sufficient to solve defined behavioral tasks, given architectural constraints informed by knowledge of mushroom body circuitry. Model dopamine neurons exhibit diverse tuning to task parameters while nonetheless producing coherent learned behaviors. Notably, reward prediction error emerges as a mode of population activity distributed across these neurons. Our results provide a mechanistic framework that accounts for the heterogeneity of dopamine activity during learning and behavior. Author summary Dopamine neurons across the animal kingdom are involved in the formation of associative memories. While numerous studies have recorded activity in these neurons related to external and predicted rewards, the diversity of these neurons’ activity and their tuning to non-reward-related quantities such as novelty, movement, and internal state have proved challenging to account for in traditional modeling approaches. Using a well-characterized model system for learning and memory, the mushroom body of Drosophila fruit flies, Jiang and Litwin-Kumar provide an account of the diversity of signals across dopamine neurons. They show that models optimized to solve tasks like those encountered by flies exhibit heterogeneous activity across dopamine neurons, but nonetheless this activity is sufficient for the system to solve the tasks. The models will be useful to generate testable hypotheses about dopamine neuron activity across different experimental conditions.