Christopher Currin is a NOMIS Fellow at the Institute of Science and Technology Austria (ISTA), working closely with the research groups of Tim Vogels (Computational Neuroscience and Neurotheory) and Gaia Novarino (Genetic and Molecular Basis of Neurodevelopmental Disorders).
Born in South Africa, Currin received a BS in biochemistry, computer science and psychology in 2013 from Rhodes University (South Africa), and a PhD in neuroscience from the University of Cape Town (South Africa) in 2020. He has received numerous awards, including the Alfred Beit award for best undergraduate performance at Rhodes University, a prestigious DAAD-NRF Joint Scholarship for masters and doctoral research, as well as visits to the University of Oxford and Technische Universität Berlin. In parallel, Currin has played an active leadership role in growing computational neuroscience and machine learning in Africa through the IBRO-Simons Computational Neuroscience Imbizo and Deep Learning Indaba X initiatives.
Currin’s doctoral studies focused on experimentally informed computational models of brain disorders like epilepsy that allow the exploration of better pharmacological treatment regimes. Currin also has extensive industry experience in data analysis and systems architecture, creating engineering solutions for a range of problems. As a NOMIS Fellow, Currin will use software engineering, machine learning and computational models to study the emerging dynamics of human neural networks from healthy subjects as well as people with epilepsy and autism spectrum disorder (ASD). His project, Unlocking Crucial Cortical Connections in Human Neural Dynamics for Health and Disorder, in collaboration with the Vogels lab, will utilize recordings from high-density multi-electrode arrays (HD-MEAs), monitoring thousands of neurons simultaneously from human-induced pluripotent stem cell (iPSC)-derived cultures that are developed in the Novarino lab at ISTA. Using these recordings, Currin and colleagues will guide the future of model development by building human data-driven biological neural network models. Employing advanced genetic and computational techniques, the researchers aim to determine how neurons connect and maintain their connections to form functional networks. This collaborative work will help to reveal the differences between natural and disease dynamics and contribute to effective lifelong clinical treatments for “plasticity pathologies” such as epilepsy and ASD.