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The study investigates how intrinsic motivation drives autonomous behavior in fruit flies by combining whole-brain recordings with a neuromechanical digital twin. It shows that a hierarchical policy selecting from low‑level motor primitives, trained with 20 intrinsic motivation algorithms, can reproduce fly locomotor statistics and internal representations of future actions. The DIAYN algorithm, which maximizes behavioral entropy and sensory separability, best matches fly behavior and neural coding, capturing individual variability and interhemispheric kinematic encoding.
arXiv:2609.17325v1 Announce Type: new Abstract: Biological cells can be viewed as individual, interacting agents whose collective dynamics give rise to adaptive behaviour at multiple levels of organi...
arXiv:2602. 17997v3 Announce Type: replace Abstract: Animals perform coordinated whole-body movements under the control of neural systems shaped by brain-wide connectivity.
arXiv:2606. 20858v2 Announce Type: replace Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored.
arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.
The article introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, along with representational selection mechanisms that govern adaptive responses. The model binds exteroceptive and interoceptive signals, bodily-motivational context, and memory traces into associative structures called semblions, enabling motivated learning that incorporates need thresholds, goal generation, and regulatory constraints.