Large AI systems are getting better at reasoning and problem-solving, but their energy cost is a serious bottleneck. Training and running today’s models—especially big neural networks and large language models—can require vast computational resources. In contrast, the human brain delivers powerful cognition using remarkable efficiency, operating at roughly 20 watts. Seeking inspiration from that efficiency, researchers at Graz University of Technology, together with international partners, have built a brain-inspired AI model designed to plan flexibly while consuming substantially less energy.
The work follows the idea that neural planning does not rely on brute-force computation to reach a solution. Instead, it uses principles observed in the brain—particularly mechanisms linked to the hippocampus—to generate candidate futures and refine actions toward a goal. “The brain works in a completely different way to today’s AI systems,” explains Wolfgang Maass from TU Graz, emphasizing an algorithmic translation of biological computation.
At the core of the approach are three interacting mechanisms. First, the model forms cognitive maps, converting relationships among abstract entities into geometric structure within neural codes—effectively providing a “sense of direction.” Second, it uses stochastic neural computations that continuously propose hypothetical scenarios, enabling the system to explore without calculating every pathway. Third, it applies compositional coding, breaking down information and action plans into reusable components.
Together, these mechanisms allow the model to “imagine” possible routes and test them in imagination before committing. Rather than searching exhaustively, the system samples intermediate steps: when a randomly selected move appears to align with the goal, guided by the cognitive map, that direction is pursued. At each new position, the model again evaluates options, gradually steering toward the target while maintaining flexibility.
A key claim is adaptability. Because planning is driven by sampling from cognitive maps and compositional representations, the system can respond to changed or newly introduced situations without requiring retraining. This capability contrasts with many conventional pipelines that must be retrained to handle new environmental structures or objectives.
To demonstrate the idea, the team evaluated the model on three challenges: navigating a two-dimensional space, orienting within an abstract multi-dimensional space, and assembling or disassembling a silhouette built from modular blocks. Across tasks, the system showed goal-directed planning behavior consistent with a sampling-and-map framework.
The researchers stress that the approach is not meant to replace today’s large language models. Instead, it proposes an alternative route for applications where efficient local decision-making matters. With further development, brain-inspired planning systems could broaden AI beyond cloud-scale compute.
Such energy-aware methods could be especially valuable for robots, autonomous vehicles, and edge devices—settings where hardware constraints demand low power operation. The study was conducted with collaborations including Tsinghua University and Italy’s National Research Council.
Subject of Research: Brain-inspired neural planning and cognitive-map-based sampling
Article Title: Neural sampling from cognitive maps enables goal-directed imagination and planning
News Publication Date: 21-Jul-2026
Web References: http://dx.doi.org/10.1038/s42256-026-01254-4
References: Nature Machine Intelligence (DOI: 10.1038/s42256-026-01254-4)
Image Credits: Lunghammer – TU Graz
Keywords
Brain-inspired AI; cognitive maps; neural sampling; stochastic planning; compositional coding; energy-efficient machine intelligence
Tags: biological computation in AIbiologically inspired machine learningbrain-inspired AIcognitive map neural networksenergy-efficient artificial intelligenceflexible problem solving AIhippocampus-inspired AI mechanismsinternational research on brain-inspired AIneural code geometric structuresneural planning modelsscalable low-energy AI systemsstochastic neural computation
