19 July 2023
Neuro-Inspired Topology and Machine Learning
at CNS 2023 in Leipzig, Germany
Workshop Description
Mathematics, machine learning, and computational neuroscience have long been intertwined. Mathematical tools such as graph theory, information theory and dynamical systems have been used to model neural activity. Similarly, modern machine learning approaches have been inspired by neuroscience and have been used to better understand brain connectivity and dynamics. These three research facets play off of each other to advance each field individually and concurrently. The goal of this workshop is to bring computational neuroscientists, mathematicians, and computer scientists together to communicate research around their expertise. Additionally, it will serve as a venue to discuss avenues of future research impacting computational neuroscience and mathematics while advancing machine learning and artificial intelligence methods.
Note, this workshop is being held in conjunction with CNS 2023. Full conference schedule can be found here.
Speakers
Maxim Bazhenov
University of California San Diego
Vasileios Christopoulos
University of California Riverside
Claudia Clopath
Imperial College London
Daniela Egas-Santander
Blue Brain Project
Celia Hacker
MPI MiS
Erik Hermansen
Norwegian Univ. of Science and Technology
Bill Lytton
Downstate Health Sciences University
Robert McDougal
Yale University
Aitor Morales-Gregorio
Jülich Research Centre
Manish Saggar
Stanford University
Brittany Story
University of Tennessee Knoxville
Nick Tolley
Brown University