AutoHebb introduces autonomous Hebbian networks that combine external feedback with internally generated neuromodulatory signals to gate a biologically grounded 3‑factor Hebbian learning rule. These networks, optimized via meta‑learning for fixed weights, demonstrate strong inductive biases and autonomous learning control, enabling zero‑shot inference and continual learning without external optimization. They outperform contemporary models on these tasks and can be reverse‑engineered into explicit algorithms, highlighting the importance of meta‑learning, Hebbian plasticity, and autonomous learning control.
arXiv:2608. 04358v1 Announce Type: new Abstract: Continual learning (CL) requires models to learn tasks sequentially, yet deep neural networks often suffer from plasticity loss and poor knowledge transfer, which can impede their long-term adaptability.
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.
MetaLearnNCA introduces a decentralized few‑shot meta‑learning framework that uses interacting Neural Cellular Automata (NCAs) to adapt to new tasks without backpropagation. The method splits adaptation into an Active‑NCA that performs task inference on a 2D spatial memory grid, and a learned Meta‑NCA that diffuses error residuals locally to update this grid. Experiments show competitive in‑distribution performance (96.12% on Omniglot) and superior out‑of‑distribution transfer on MNIST, KMNIST, and Fashion‑MNIST compared to canonical meta‑learners.
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:2604. 27031v2 Announce Type: replace-cross Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities.
arXiv:2606. 31700v1 Announce Type: new Abstract: Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory.
arXiv:2608. 14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses.
arXiv:2411. 16073v4 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework that adapts a frozen pre-trained Transformer through task-specific soft subnetworks: real-valued multiplicative masks over the query, key, value, and output projections of selected self-attention layers.
arXiv:2608. 06963v1 Announce Type: new Abstract: Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons.
arXiv:2503. 21796v2 Announce Type: replace-cross Abstract: Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence.
arXiv:2607. 18433v1 Announce Type: cross Abstract: Intelligence appears under different names in different fields: as data compression in statistics and machine learning, as universal computation in dynamical systems, and as adaptive behavior in agents.
arXiv:2609.35897v1 Announce Type: new Abstract: The pursuit of recursive self-improvement (RSI) toward general intelligence is divided between macro-level language model scaling and the interaction-d...