Predictive olfactory learning in Drosophila

Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

Automated summary

Olfactory learning and conditioning in fruit flies are often modelled using correlation-based associative synaptic plasticity. The connections from Kenyon cells to mushroom body output neurons play a key role in conditioning odor-evoked responses by shocks. Different models are proposed to explain how predictions of aversive or appetitive values of odors are formed on a circuit level, including error-driven predictive plasticity and target-driven predictive plasticity in dopaminergic neurons. These models provide a framework for understanding MBON circuits and interpreting DAN activity during olfactory learning.