Scalable Machine Learning

We develop machine learning and deep learning architectures that are efficient and scalable, as well as algorithms necessary to make use of scalable models for continual learning. Co-hosted with the Sustainable Machine Learning group led by Dr. David Kappel.


  • Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training
    Grappolini, E. W., & Subramoney, A.
    In International Conference on Neuromorphic Systems (ICONS ′23), Santa Fe, NM, USA ACM
  • Efficient Recurrent Architectures through Activity Sparsity and Sparse Back-Propagation through Time
    Subramoney, A., Nazeer, K. K., Schöne, M., Mayr, C., & Kappel, D.
    In International Conference on Learning Representations
  • Efficient Real Time Recurrent Learning through Combined Activity and Parameter Sparsity
    Anand Subramoney,
    In ICLR 2023 Workshop on Sparse Neural Networks


  • Memory Modules for Deep Learning
    Hark, N.
    Master’s thesis, Institute of Neural Computation, Ruhr University Bochum, Bochum, Germany

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210