Fully funded Ph.D. (or Master’s) program in the field of systems neuroscience/computational neuroscience of Drosophila

About the Project
About the Project
We study neural circuit principles of sensory perception and decision making using one of the most popular model organisms with a concise brain — the fruit fly Drosophila melanogaster. In particular, our research programs focus on understanding how flying flies stabilize its body using surrounding visual motion cue and detect attractive or dangerous visual objects. Our overall goal is to get at a system-level understanding of these brain functions by combining various experimental techniques such as electrophysiology, calcium imaging, optogenetics, and behavioral genetics. Another line of work in the lab involves developing computational models of the experimentally-derived neural algorithms, and thereby bringing new insights into engineering applications such as unmanned air vehicles (i.e., drones).

Candidate students are expected to have basic knowledge in neuroscience such as neurons, action potentials, sensory and motor systems. As the experiments are performed with custom-made stimulus patterns, and the physiological and behavioral data are required to be analyzed by a custom software, experiences on basic programming languages such as python and matlab are also important.

From this research program, students will acquire knowledge on advanced experimental techniques in neuroscience as well as data analysis techniques.

Visual Processing

We can almost effortlessly recognize others’ faces from complex backgrounds and distinguish subtle differences in facial expressions. Such abilities can easily outperform state-of-the-art computer algorithms. How does a set of brain cells give rise to such a remarkable feat? We approach this question by studying the fruit fly’s visual system. This tiny insect exhibits a large repertoire of higher-order visuomotor functions, including object recognition/tracking, object avoidance, object distance estimation, selective attention, place learning, and gap width estimation. In our lab, we study how such visual features are represented in a neural circuit and triggers specific behavioral programs.

Flight Control

Fruit flies are agile fliers. With wings that beat 200 times a second, they can hover, fly straight, avoid collision, turn rapidly, and even land on an improbable target. To make these sophisticated flight maneuvers, fruit flies constantly make use of their sensory feedback. In two previous studies (Cell 2017 and Nat Neurosci 2015), we demonstrated how a set of visual neurons guides fruit fly’s stabilization behaviors and how their visual signaling is quantitatively modulated by internal predictions to enable rapid flight turns. We aim to develop this study further by uncovering the detailed circuit mechanism of internal predictions.

Brain-inspired models

Flies’ flight abilities outperform any man-made flying device, in terms of their stability, adaptability, maneuverability, and robustness.  For this reason, quantitative analyses of neural mechanisms underlying flies’ flight control is likely to help developing new solutions for man-made robots. In particular, the forward model can predict sensory feedback caused by a self-generated action, and that prediction can be used to selectively pass or block the neural signals from the sensory periphery. Currently, we are developing a computational model of this control mechanism to apply it to simple engineering systems, such as an inverted pendulum or an unmanned air vehicle (i.e., drone).

Our University

Hanyang University is a private research university in South Korea. The main campus is in Seoul, and the second one, the Education Research Industry Cluster at Ansan, or ERICA campus, is located in Ansan.

Hanyang University has an alumni network of 330,000 that is not limited to the field of engineering but also to other fields. In 2019, QS World University Rankings ranked Hanyang University 150th.

General requirements and application procedure

We are looking for highly motivated, creative students with a Bachelor’s or Master’s degree in a relevant discipline (biology, neuroscience, electrical engineering or equivalent). They must also have a good understanding of basic mathematics and programming languages such as matlab or python.

Applicants should send their cover letter, CV including relevant experience for the position and relevant academic courses, and transcripts (undergraduate and Master’s) all in PDF format via e-mail to [email protected]. Recommendation letters sent by the applicant will be considered.

Information about the research details at professor Anmo Kim’s laboratory is available at the following:

https://www.anmokimlab.com/