Drosophila (a fruit fly larva) is a small crawling animal that has a brain with about 10,000 neurons. The wiring diagram of its neurons has been reconstructed at the synaptic level. The morphologies of all the neurons are identified and preserved across larvae, making it possible to relate their identity to their function in the circuit. The latest genetic tools for imaging activation or inactivation can be swiftly expressed into neurons of interest, allowing optical access to any neuron in an intact animal through its transparent cuticle. This makes larval Drosophila a powerful model to study neural computations at the circuit level.
Fruit fly larvae navigate their sensory environment to seek food and avoid danger. To survive, they must respond appropriately to a range of sensory cues, all of which contain noise and may pre-sent conflicting information.
How does a simple brain perform this rich processing task?
Flylight is a valuable resource for the Drosophila community as it offers a comprehensive library of genetic lines to help identify neurons of interest.
Confocal stack of larval central nervous system, reveals its memory and learning center, known as the mushroom body (shown in green).
The genetic tools available in Drosophila, combined with protein engineering and the completed connectome, enable us to activate, inactivate, or image specific neurons, helping us uncover their roles in sensory-to-motor information processing.
In our lab we aim to understand how brains integrate variable, noisy, and often conflicting information. This requires an integrated approach: describe a behavior, identify which neurons are involved, resolve how circuit activity encodes those behaviors, and find the mechanisms generating these neural transformations.
Larvae navigate by performing a series of forward runs, interspersed with reorienting turns. They adjust the frequency, size, and direction of these turns to reach a specific navigational goal. How do animals modify their behavior based on prior experiences? Derick is investigating how larval fruit flies alter their navigation strategies in taste environments, focusing on how previous experiences influence their decision-making and movement patterns.
With these assays we can deliver controlled temporal patterns of stimuli to many animals simultaneously and quantify their behaviors (input-output functions). Reverse correlation assays that use white noise stimulus reveal the logic of sensory-to-motor transformations.
Using optogenetic tools we can activate neurons using red light. This allows us to preturb activity of targeted neurons during decision-making to reveal how different neurons' activity contributes to animals' behavior.
Outrech project: "Sweet for hearts". Larvae are placed on the arena where heart-shaped area is infused with fructose and the rest is infused with salty solution. Tracks reveal that larvae (mostly) linger within the sweet heart-shaped area.
Variability is a defining feature of biological computation. Even a simple organism like the larva responds unreliably to identical stimulus presentations. For example, when faced with a sudden increase in light, larvae tend to stop moving and initiate a turn but sometimes apparently ignore the change and continue to crawl forward. Why? Is that due to sensory perception or does the variability originate downstream? Where are decisions made? We seek to understand the logic behind behavioral responses of individuals to identical repeated uni- and multi-modal stimulation. We will then use the two-photon tracking microscope to look under the hood and correlate neural patterns with behavioral outcomes.
First of its kind two photon microscope that allows direct imaging of neural patterns that encode behavior while the larva is freely moving and freely behaving.
We are implementing additional features to the volumetric two-photon tracking microscope that will allow us to activate targeted neurons while imaging patterns of neural activity in a freely behaving animal.
Dual-color imaging of premotor neurons in a freely locomoting fruit fly larva reveals wave-like activity patterns encoding larva’s peristatic locomotion.
