Professor – Department of Bioengineering and Department of Neuroscience
University of Pennsylvania
Rethinking the role of machine learning in neuroscience”
The goal of much of computational biology is to numerically describe data from a system, but also to find ways of fixing it and to understand a system’s objectives, algorithms, and mechanisms. Here we will argue that, regardless the objective, machine learning should be a central contribution to progress in every flavor of biomedical science. Machine learning can typically better describe the data. In doing so it can also provide a benchmark for any other way of describing the data. Using examples from neuroscience we discuss how better performance matters for decoding models and how having a benchmark affects encoding models. Similar issues matter in medicine. As biomedical science evolves, machine learning is morphing into a critical tool across the full spectrum of scientific questions.
Eef Joosten: firstname.lastname@example.org
Cecile Issard: email@example.com