Characterization of Visual Feature Encoding in Drosophila Visual Projection Neurons LPLC1 and LPLC2

Monday, September 15, 2025

11:00 AM-1:00 PM

BIOMED PhD Research Proposal

Title: 
Characterization of Visual Feature Encoding in Drosophila Visual Projection Neurons LPLC1 and LPLC2

Speaker:
Bryce Hina, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Catherine von Reyne, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Details:
A critical function performed by visual systems is the fast and reliable detection of features within the environment, such as moving objects or approaching threats. Neurons predicted to encode these higher order, behaviorally relevant visual features have been identified in a variety of species. However, a lack of cell-type specific genetic tools, accessible neural anatomy, and clearly mapped neural circuitry has limited the identification of these cell-types and led to an incomplete understanding of the mechanisms which govern feature encoding in visual systems. Here, we investigate visual feature encoding by leveraging Drosophila melanogaster. We specifically investigate visual feature encoding in visual projection neurons (VPNs), a class of neurons predicted to selectively encode visual features. We focus on two VPN populations, Lobula Plate/Lobula Columnar type 1 (LPLC1) and LPLC2. Differences in stimulus parameters, recording locations, and recording methodologies have resulted in conflicting findings regarding the feature selectivity of these neurons. 

Currently, it’s unclear if these neurons selectively encode features of objects approaching on a collision course, or if they also encode features of other visual stimuli, such as translating objects. Further, the indirect nature of previous recording methods has proven inadequate to resolve the differences between LPLC1 and LPLC2 encoding or determine whether they encode visual features using graded potentials or spiking. Our preliminary data demonstrate clear spiking activity within these neurons, contrary to prior assumptions, suggesting feature selectivity may emerge from a spike timing code, which has to date been ignored. Thus, there is a critical need to study these neuron populations directly using whole-cell electrophysiology and precisely designed stimuli to reveal their selectivity to specific visual features and understand how these features are encoded. The goal of this project is to uncover feature tuning for these neurons at the level of individual spikes and investigate their feature selectivity. To accomplish this goal, we will first investigate the spiking properties and map the receptive fields of LPLC1 and LPLC2 (Aim 1). Following this, we will investigate their tuning to different visual stimuli with varied size and speed parameters (Aim 2). Finally, we will investigate how neuron active properties further govern feature selectivity within these neurons through multicompartment modelling (Aim 3). Findings from our work will reveal the specificity and underlying mechanisms for feature encoding within two VPN types, which may serve as a foundation for investigating feature detecting neurons in other, more complex species. Because the Drosophila visual system shares organizational principles with vertebrates, our findings will provide a foundation for understanding feature-detecting neurons across species and advance general principles of visual computation.

Contact Information

Natalia Broz
njb33@drexel.edu