Translate frontier machine-learning prototypes into scalable systems for drug discovery. The role optimises models such as diffusion networks, transformers and DNA-sequence models in JAX or PyTorch, improves distributed training and inference, and develops research libraries and evaluation tools.
ePoster · Computational Neuroscience
Toward a biophysically-detailed, fully-differentiable model of the mouse retina
Kyra Kadhim, Ziwei Huang, Michael Deistler, Jonas Beck, Thomas Euler, Jakob Macke, Philipp Berens · Bernstein Conference 2024
Advancements in machine learning have drastically improved our ability to fit biophysical models of neural systems. We present here a fully-differentiable, biophysical model of the mouse retina that is primed for gradient-based optimization. Our model is based on the Jaxley software library (Deistler et al. 2024, Bernstein Conference) and JAX machine learning framework. The model consists of a network of Hodgkin-Huxley style neurons, realistic synapse dynamics, an experimentally-informed organization of cells in 3D space, and spatially constrained connectivity. We further implemented a wide v