Publications by Semih Günel | EPFL Graph Search

Microengineered devices enable long-term imaging of the ventral nerve cord in behaving adult Drosophila

Pavan P Ramdya, Mahmut Selman Sakar, Chin-Lin Chen, Murat Kaynak, Semih Günel, Victor Lobato Rios, Laura Joan Hermans, Florian Aymanns, Jonas Friedrich Braun

The dynamics and connectivity of neural circuits continuously change on timescales ranging from milliseconds to an animal's lifetime. Therefore, to understand biological networks, minimally invasive methods are required to repeatedly record them in behavin ...

Nature Research2022

Using Animal Motion Capture to Learn Neural Representations

Semih Günel

Understanding behavior from neural activity is a fundamental goal in neuroscience. It has practical applications in building robust brain-machine interfaces, human-computer interaction, and assisting patients with neurological disabilities. Despite the eve ...

EPFL2022

Overcoming the Domain Gap in Neural Action Representations

Pascal Fua, Pavan P Ramdya, Semih Günel, Florian Aymanns, Sina Honari

Relating behavior to brain activity in animals is a fundamental goal in neuroscience, with practical applications in building robust brain-machine interfaces. However, the domain gap between individuals is a major issue that prevents the training of genera ...

SPRINGER2022

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Victor Lobato Rios, Daniel Eduardo Morales Garza, Marco Pietro Abrate, Adám Gosztolai

LiftPose3D infers three-dimensional poses from two-dimensional data or from limited three-dimensional data. The approach is illustrated for videos of behaving Drosophila, mice, rats and macaques. ...

NATURE PORTFOLIO2021

Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Mirela Ostrek, Siyuan Li

Our goal is to capture the pose of neuroscience model organisms, without using any manual supervision, to be able to study how neural circuits orchestrate behaviour. Human pose estimation attains remarkable accuracy when trained on real or simulated datase ...

2020

Gravity as a Reference for Estimating a Person's Height from Video

Pascal Fua, Didier Bieler, Helge Jochen Rhodin, Semih Günel

Estimating the metric height of a person from monocular imagery without additional assumptions is ill-posed. Existing solutions either require manual calibration of ground plane and camera geometry, special cameras, or reference objects of known size. We f ...

IEEE2019

What Face and Body Shapes Can Tell Us About Height

Pascal Fua, Helge Jochen Rhodin, Semih Günel

Recovering a person's height from a single image is important for virtual garment fitting, autonomous driving and surveillance. However, it is also very challenging without absolute scale information. Here, we examine the rarely addressed case, where camer ...

IEEE COMPUTER SOC2019

DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Daniel Eduardo Morales Garza, João Henrique Campagnolo

Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. Ho ...

2019