Neuroengineering Laboratory ‐ EPFL

Neuroengineering Laboratory

Welcome to the Ramdya Lab, Firmenich Next Generation Chair of Neuroengineering

We are part of the Brain Mind Institute and Institute of Bioengineering in the School of Life Sciences. In the Neuroengineering Laboratory, we are reverse-engineering vinegar flies (Drosophila melanogaster) to better understand the mind and to design more intelligent robots. Flies are an ideal model for reverse-engineering: they generate complex behaviors, their nervous systems are small, and they are genetically malleable. Our lab develops and combines microscopy, machine learning, genetics, and computational models to address systems-level questions. Enjoy your visit!

Some of the tools we use

Recording neural activity during behavior

We combine genetics, 2-photon microscopy, and machine learning-based image analysis to establish links between neural activity and behavior. Relevant publications: Hermans, Kaynak et al., bioRxiv 2021, Chen, Hermans et al., Nature Communications 2018

Building computational models of Drosophila

We have developed computational simulations to capture the core neuromechanical mechanisms that orchestrate behavior. These models synthesize our biological data, generate predictions for future experiments, and inspire the development of bioinspired robotic controllers. Relevant publications: Lobato et al., Nature Methods, 2022, Ramdya, Thandiackal et al., Nature Communications, 2017

Drawing causal links between neuronal activity and behavior

We use deep-learning based pose estimation and optogenetic neuronal activation and silencing to quantify the causal links between neural activity and behavior. Relevant publications: Gosztolai, Günel et al. Nature Methods 2021, Günel et al. Elife 2019

Robotic experimental automation

We build robotic systems and computational data analysis pipelines to automate high-throughput biological experiments. This allows us to rapidly screen behavioral phenotypes. Relevant publications: Ramdya et al., Nature 2015, Maesani, Ramdya et al., PLoS Computational Biology 2015