MIND initiative: Fachbereich Humanwissenschaften

Mission

An interdisciplinary research initiative aiming to understand intelligence in biological and artificial systems.

MIND is a research initiative connecting artificial intelligence, cognitive science and neuroscience to build formal models of intelligence, while using AI as a partner for neuroscientific discovery.

The pursuit of artificial intelligence and the understanding of biological intelligence are deeply intertwined. AI offers formal models and simulation tools whereas cognitive and neuroscientific insights point toward new ways to overcome limitations of today's systems.

MIND creates a visible interdisciplinary home for this work at Osnabrück University, linking computational models, human and neural data, experimental design and modern machine learning.

Participating Research Groups

Cognitive Modelling
Computational Neuroscience
Machine Learning
Natural Language Processing
Neurobiopsychology
Neurocomputation
Neuroinformatics

News and Events

MIND Lecture series:
Thousand-Brains Systems: Sensorimotor Intelligence for Rapid, Robust Learning and Inference
Viviane Clay (Executive Director of the Thousand Brains Project (Numenta))
August 24th, 2026, 1pm sharp, 93/E44
Our publication was featured on the cover of Nature Machine Intelligence (May 2026 issue):
Lu, Z., Thorat, S., Cichy, R.M. et al.
Adopting a human developmental visual diet yields robust and shape-based AI vision. Nat Mach Intell 8, 735–748 (2026).

Want to explore more?
Browse our full range of outputs and events on the Activities page.

One initiative, three synergistic directions

Neuroscientific research

We unlock new insights into the computational and biological underpinnings of cognition via neuroscientific and behavioural experimentation, novel data analysis techniques, and large-scale computational modelling.

Ein Mädchen und ein Roboter bauen einen Turm mit Bauklötzen.

Artificial intelligence

We are working towards more efficient, human-aligned AI systems by taking inspiration from the brain. In doing so, we combine expertise in neuromorphic computing, modelling of dendritic computation, Bayesian modelling techniques, and sensory-grounded large-scale models of cortical computations.