Andreas Schneider

Education:

• B.Sc. Physik, University of Bayreuth

  Thesis topic: Bau und Charakterisierung von organisch-anorganisch perowskitbasierten Leuchtdioden

• M.Sc. Physics, University of Göttingen

  Thesis topic: Information Dynamics and Fine Tuning of Single Components in Artificial Neural Networks

• Semester Abroad 2015/16 Universidad Nacional de Colombia, Bogotá, Colombia
• Internship Abroad 2017 Sirena Technologies, Bangalore, India

"Information and Learning Rules in Artificial Neural Networks"

My research is centered around 'infomorphic' neural networks, a novel framework for local learning that incorporates a wide variety of known strategies. Inspired by the self-organization of living neurons, this approach identifies objective functions for individual artificial neurons in order to create more robust and interpretable learning algorithms.

Self-organization of Information Processing, Information Theory, Neural Networks, Collective Intelligence

• Ehrlich, D. A., Schneider, A. C., Priesemann, V., Wibral, M., & Makkeh, A (2023). A Measure of the Complexity of Neural Representations based on Partial Information Decomposition. Transactions on Machine Learning Research.
• Makkeh, A., Graetz, M., Schneider, A. C., Ehrlich, D. A., Priesemann, V., & Wibral, M. (2023) . A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions.
• Schneider, A. C., Neuhaus, V., Ehrlich, D. A., Makkeh, A., Ecker, A. S., Priesemann, V., & Wibral, M. (2024). What should a neuron aim for? Designing local objective functions based on information theory.

Teaching

• Deep Learning for Computer Vision, Winter 2022/23
• Machine Learning, Summer 2022
• Introduction to Physics of Complex Systems, Winter 2019/20
• Scientific computing, Summer 2019

Conferences

• From Neuroscience to Artificially Intelligent Systems, 2022 in Cold Spring Harbor, NY, USA
• Computational Neuroscience Conference, 2023 in Leipzig
• Bernstein Conference, Workshop Organizer, 2024 in Frankfurt
• Guided Self-Organization Workshop, 2025 in Tübingen