How can machine learning be used to generate insights and theories in neuroscience?
Organizers
Alexander Ecker | University of Göttingen, Germany
Fabian Sinz | University of Göttingen, Germany
Mohammad Bashiri, Pavithra Elumalai, Michaela Vystrcilová | University of Göttingen, Germany
Abstract
Machine Learning (ML) as a tool for investigating the mechanisms underlying brain function has been a topic of intense debate in recent years. Some researchers argue that ML and Artificial Neural Networks (ANNs) as models of the brain can offer deep insights into the computations carried out by neuronal populations, while others view these models as black boxes that provide only limited understanding of neural processes. With this workshop, we aim to bring together experts from both sides of this debate for a stimulating and productive discussion. Our goal is to explore the potential of ML for generating concrete theories and insights in neuroscience. To facilitate this conversation, we have invited a diverse group of speakers with contrasting perspectives on the topic. Each speaker will deliver a talk presenting their views and research findings, followed by a panel discussion moderated by Paul Middlebrooks, the host of the Brain Inspired podcast.
