arXiv Machine Learning
Jun 5
Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies
arXiv:2602. 01196v2 Announce Type: replace Abstract: Recurrent neural policies are widely used in partially observable control and meta-RL tasks.
An Energy-Based Mechanism for Compositional Behavior
The paper proposes a single variational principle that explains how gating mechanisms, their dynamics, and neural implementations for behavioral composition can be unified. This principle yields softmax gating, an energy‑based dynamical system with guaranteed convergence, and a recurrent neural network model with context‑dependent, local interactions. Experiments across collective behavior, human decision‑making, and layered control show that the mechanism reproduces known behavioral patterns, offers interpretable accounts of behavior combination, and matches or outperforms existing methods.
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arXiv AI
Jun 18
Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion
arXiv:2606. 18469v1 Announce Type: cross Abstract: Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms.
arXiv AI
Aug 11
Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence
arXiv:2511. 10119v4 Announce Type: replace Abstract: We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM).
arXiv AI
Jul 15
Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents
arXiv:2412. 04847v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has demonstrated remarkable capabilities in training agents to solve complex tasks autonomously, such as mobile robots, UAVs/UGVs, and game-playing agents).
arXiv Machine Learning
Sep 23
Brain-Inspired Hierarchical Modularity for General Continual Learning
arXiv:2609.25146v1 Announce Type: new Abstract: Continual learning, the ability to learn from sequential experience while retaining and adapting prior knowledge, is central to intelligent systems ope...
arXiv AI
Sep 16
Intrinsic Motivation in Reinforcement Learning: A Research Agenda for Adaptive Self-Organisation
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 Machine Learning
Jul 13
Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning
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 Machine Learning
Sep 16
Bio-Inspired Palette Evolution in Indirectly Encoded Substrates: Timescale Compatibility Shapes Activation Function Discovery
arXiv:2609.17067v1 Announce Type: cross Abstract: Indirectly encoded neural networks can assign different activation functions to individual nodes, but the right functions are rarely known in advance...
arXiv Machine Learning
Jul 15
Constructed Reality, Contested Priors: Decoupling and the Architecture of Cognitive Relapse Under the Free Energy Principle
arXiv:2607. 11958v1 Announce Type: new Abstract: Under the free energy principle, a predictive system does not observe reality directly; it maintains a generative model of the world and experiences that model's best current hypothesis.
arXiv Machine Learning
Aug 17
Emergent Models: Intelligence from Tiny Substrates
arXiv:2608. 14019v1 Announce Type: cross Abstract: Emergent Models (EMs) are a machine learning paradigm based on simple yet open-ended substrates, such as cellular automata, in which modeling is treated not as the learning of a closed-form input-output map but as the emergence, within simple dynamical systems, of computational behaviors that solve external tasks.
arXiv AI
Sep 16
Beyond Episodic AI: Cognitive Field Networks for Biologically Inspired Persistent Cognition
arXiv:2609.16752v1 Announce Type: new Abstract: Cognitive Field Theory (CFT) proposes that cognition arises from memory-dressed collective dynamics that generate a persistent macroscopic cognitive fi...
arXiv Machine Learning
Sep 22
Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics
The paper investigates how multi‑neuromodulatory dynamics—driven by dopamine, acetylcholine, serotonin, and noradrenaline—can improve artificial neural networks’ continuous adaptive learning and mitigate catastrophic forgetting. It demonstrates that neuromodulators interact across multiple spatial and temporal scales, creating a complex many‑to‑many mapping between neuromodulators and tasks. The authors propose neuromodulation‑aware learning rules, architectural formulations, and a conceptual Go/No‑Go study to illustrate how biologically inspired mechanisms can enhance ANN robustness and adaptability.