Panagiotis Sakagiannis

This paper introduces a hierarchical model architecture for simulating Drosophila larva locomotion and foraging behaviors, enabling comparisons between empirical and simulated data.

The paper shows that feeding state modulates inhibitory neurons in Drosophila, biasing sensorimotor decisions by altering aversive response competition.

Larvaworld is a virtual laboratory platform for Drosophila larvae that integrates agent-based modeling with neural control and analysis of simulated and experimental data.

The paper investigates temperature preference behavior evolution in Drosophila species by analyzing over 2400 larvae and using agent-based modeling to show that behavior differences arise from circuit balance, not sensitivity changes.

The paper develops a spiking neural network model of Drosophila larva olfactory learning that uses prediction error to drive associative learning and behavior.