Home | Institut für Neuroinformatik

RUB
Institut für Neuroinformatik

Upcoming Events

To be defined

Oct 21, 12:15 PM
/
NB 3/57, online

Shirin Reyhanian, M.Sc.

To be defined

Oct 28, 12:15 PM
/
NB 3/57, online

Carlos Antolín Fuentes, M.Sc.

INI colloquium by Genela Morris: To be defin

Nov 4, 12:15 PM
/
NB 3/57, online

Talk by Artur Pilacinski: To be defined

Nov 11, 12:15 PM
/
NB 3/57, online

Usage of AI in Science V: Workflow Exchange

Nov 18, 12:15 PM
/
NB 3/57, online

Pavlos Rath-Manakidis, M.Sc.

Latest Publications

Probing Length Generalization in Mamba via Image Reconstruction
In L. Pasa, Lintas, A., Tetko, I. V., Micheli, A., Navarin, N., Villa, A. E. P., et al. (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2026 (pp. 235–246) Cham: Springer Nature Switzerland

Rathjens, J., Schiewer, R., Wiskott, L., & Subramoney, A.. (2027). Probing Length Generalization in Mamba via Image Reconstruction. In L. Pasa, Lintas, A., Tetko, I. V., Micheli, A., Navarin, N., Villa, A. E. P., et al. (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2026 (pp. 235–246). Cham: Springer Nature Switzerland.
Perceptually Grounding and Determining Truth Values of Propositions Joined by Logical Connectives: A Neural Process Account
In Proceedings of the 48th Annual Conference of the Cognitive Science Society

pdf

Akinci, L. B., Kang, M., & Schöner, G.. (2026). Perceptually Grounding and Determining Truth Values of Propositions Joined by Logical Connectives: A Neural Process Account. In Proceedings of the 48th Annual Conference of the Cognitive Science Society.
A Computational Framework for Social Modulation of Generative Episodic Memory
In NeurIPS 2026 Workshop: Personalized, Aligned, Long-Term Memory for AI Systems

Altamimi, A., Reyhanian, S., Fayyaz, Z., Echterhoff, G., Cheng, S., & Wiskott, L.. (2026). A Computational Framework for Social Modulation of Generative Episodic Memory. In NeurIPS 2026 Workshop: Personalized, Aligned, Long-Term Memory for AI Systems. Retrieved from https://openreview.net/forum?id=DIGJJmrVOj
Understanding Transformer-Based Vision Models via Modular Feature Inversion
Transactions on Machine Learning Research

link

Rathjens, J., Reyhanian, S., Kappel, D., & Wiskott, L.. (2026). Understanding Transformer-Based Vision Models via Modular Feature Inversion. Transactions on Machine Learning Research. Retrieved from https://openreview.net/forum?id=O5sMv2o3EV
Analysis of the Effect of Multiplicative Noise on the $(μ/μ_w, λ)$-ES Under Two Noise Models
In Parallel Problem Solving from Nature – PPSN XIX (Vol. 16987, pp. 18–33) Trento, Italy: Springer

pdf

Jungeilges, A., Chotard, A., & Auger, A. (2026). Analysis of the Effect of Multiplicative Noise on the $(μ/μ_w, λ)$-ES Under Two Noise Models. In Parallel Problem Solving from Nature – PPSN XIX (Vol. 16987, pp. 18–33). Trento, Italy: Springer. http://doi.org/10.1007/978-3-032-36226-1_2

Upcoming Teaching Events

Mathematics for Modeling and Data Analysis

Oct 15, 14:15
/
NB 3/57

Lecture

Learning and Decision Making in Brains and...

Oct 19, 16:00
/
IC 03/134

Seminar

Introduction to Bayesian modeling

Oct 20, 10:00
/
NB 3/72

Seminar

Machine Learning: Unsupervised Methods (Theory)

Oct 20, 10:30
/
NB 3/57

Self-study

The Institut für Neuroinformatik (INI) is a research unit of the Faculties of Computer Science and Medicine at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and 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 psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

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

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

sekretariat@ini.rub.de
www.ini.rub.de

Latest News

Successful PhD Defense: Machine Learning Applied in Hybrid Models for Optimising Waste Sorting Processes

2026 Nobel Prize in Physiology or Medicine for Optogenetics

Successful PhD Defense: Computational Modeling of Generative Episodic Memory

Successful PhD Defense: On the spectral theory of Markov chains and its application to Slow Feature Analysis

Successful PhD defense: Modeling the function of episodic memory in spatial navigation: a reinforcement learning approach

Upcoming Events

To be defined

To be defined

INI colloquium by Genela Morris: To be defin

Talk by Artur Pilacinski: To be defined

Usage of AI in Science V: Workflow Exchange

Latest Publications

Upcoming Teaching Events

Mathematics for Modeling and Data Analysis

Numerical Optimization

Informatik 1 - Programmieren

Introduction to Data Science

Learning and Decision Making in Brains and...

Introduction to Bayesian modeling

Informatik 1 - Programmieren

Machine Learning: Unsupervised Methods (Theory)

About the INI

Contact