Rainer Engelken

Education

• Ph.D. (Dr. rer. nat.), Physics, University of Goettingen, 2017. Research conducted at the Max Planck Institute for Dynamics and Self-Organization.
• Diploma (Physics, M.Sc. equivalent), University of Tuebingen, 2011
• Certificate (Advanced Mathematics, MA equivalent), University of Cambridge, 2009

Academic Positions

• Assistant Professor, Department of Electrical and Computer Engineering
• Assistant Professor (Affiliate), Department of Computer Science
• Assistant Professor (Affiliate), Coordinated Science Laboratory

Research Interests

• Neural Data Analysis
• Dynamical Systems Theory
• Machine Learning and Artificial Intelligence
• Computational Neuroscience
• Theoretical Neuroscience

Selected Articles in Journals

• Engelken R., Wolf F., Abbott L. F. Lyapunov spectra of chaotic recurrent neural networks. Phys. Rev. Research 5:043044, 2023
• Palmigiano A., Engelken R., Wolf F. Boosting of neural circuit chaos at the onset of collective oscillations. eLife, 2023
• Engelken R., Ingrosso A., Khajeh R., Goedeke S., Abbott L. F. Input correlations impede suppression of chaos and learning in balanced rate networks. PLOS Computational Biology 18(12):e1010590, 2022
• Engelken R., Farkhooi F., van Vreeswijk C., Hansel D., Wolf F. A reanalysis of “Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons”. F1000Research 5:2043, 2016
• Wolf F., Engelken R., Touzel M., Florez J., Neef A. Dynamical models of cortical circuits. Current Opinion in Neurobiology 25:228-236, 2014

Articles in Conference Proceedings

• Engelken R., Abbott L. F. Analyzing and Improving Surrogate Gradient Training in Binary Neural Networks Using Dynamical Systems Theory. ICML Workshop 'Differentiable Almost Everything', 2024
• Engelken R., Abbott L. F. Understanding and Optimizing Temporal Credit Assignment in Biological and Artificial Neural Networks using Dynamical Systems Theory. Proceedings of Cognitive Computational Neuroscience (CCN), 2024
• Engelken R. Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians. NeurIPS, 2023.
• Engelken R. SparseProp: Efficient Event-Based Simulation and Training of Sparse Recurrent Spiking Neural Networks. NeurIPS, 2023
• Engelken R., Goedeke S. A time-resolved theory of information encoding in recurrent neural networks. NeurIPS, 2022

Pending Articles

• Engelken R., Monteforte M., Wolf F. Sparse chaos in neural circuits. arXiv 2412.21188, 2024