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Unlike complex neural networks and large language models, the system employs human problem-solving strategies, making it more energy-efficient.
The capabilities of large AI systems are constantly improving, but they consume a great deal of energy during training and operation. The human brain, by contrast, is extremely energy-efficient: it requires only around 20 watts. Researchers at Graz University of Technology, in collaboration with international partners, have developed a novel, brain-inspired AI model that can plan flexibly and solve complex problems. In doing so, it consumes significantly less energy than multi-layer neural networks or large language models.
“The brain works in a completely different way to today’s AI systems,” says Wolfgang Maass from the Institute of Machine Learning and Neural Computation at Graz University of Technology. “We are trying to translate the way it works into algorithms and apply them to AI systems.”
Three mechanisms of the brain
Inspired by neuroscientific studies of the hippocampus, Wolfgang Maass and his colleague Yukun Yang have identified three mechanisms that the human brain employs in planning and problem-solving:
- the creation of cognitive maps, i.e. the transformation of relations between abstract objects into geometric relations between neural codes in the brain, that provides a “sense of direction” like a spatial map;
- stochastic neural computations, i.e. the ongoing generation of hypothetical scenarios and predictions;
- and compositional coding, i.e. the breakdown of information and action plans into reusable components.
The translation of these mechanisms into algorithms enables the Graz-based AI model – much like humans or animals – to conceive of and test possible approaches to solving complex problems without having to completely calculate them all the way to a solution. If a randomly selected intermediate step points towards the intended goal – here, the cognitive map guides the system – this path is pursued. At its new position, the system again evaluates various options for action, thereby gradually getting closer to the goal. “With this approach, our AI model can also react flexibly to changed or new situations without needing to be retrained,” says Yukun Yang.
Assembling and disassembling a silhouette
The researchers successfully tested the capabilities of their brain-inspired AI using three tasks: navigating a two-dimensional space, orienting itself in an abstract, multi-dimensional space, and assembling and disassembling a silhouette made up of various building blocks.
The researchers emphasise that their approach is not intended to rival today's large language models, but provides the basis for an alternative approach for certain applications. “We are still at a relatively early stage of development,” says Wolfgang Maass. “But our work shows that powerful AI does not necessarily require huge data centres and enormous amounts of energy.”
Suitable for robots and edge devices
In the long term, such brain-inspired systems could be used in robots, autonomous vehicles or other edge devices – in other words, wherever AI needs to operate locally with limited energy supply.
Wolfgang Maass works as a key researcher at the Bilateral AI Cluster of Excellence. The current study was conducted in collaboration with Tsinghua University and the National Research Council of Italy.
Wolfgang MAASS
Em.Univ.-Prof. Dipl.-Ing. Dr.rer.nat.
TU Graz | Institute of Machine Learning and Neural Computation
Tel.: +43 316 873 – 5822
wolfgang.maass@tugraz.at
Yukun YANG
B.E. M.S.
TU Graz | Institute of Machine Learning and Neural Computation
Tel.: +43 316 873 5842
yukun.yang@tugraz.at
Neural sampling from cognitive maps enables goal-directed imagination and planning
Authors: Hui Lin, Yukun Yang, Rong Zhao, Giovanni Pezzulo, Wolfgang Maass
In: Nature Machine Intelligence, 2026
DOI: https://doi.org/10.1038/s42256-026-01254-4
Wolfgang Maass from the Institute of Machine Learning and Neural Computation at Graz University of T ...
Quelle: Helmut Lunghammer
Copyright: Lunghammer - TU Graz
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Wolfgang Maass from the Institute of Machine Learning and Neural Computation at Graz University of T ...
Quelle: Helmut Lunghammer
Copyright: Lunghammer - TU Graz
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