The paper introduces ELiSe, a model that leverages cortical network scaffolds and dendritic compartments to learn complex non‑Markovian spatio‑temporal patterns using only local, always‑on, phase‑free synaptic plasticity. It demonstrates the model’s ability to acquire and replay intricate sequences, exemplified by a birdsong learning mock‑up, and shows robustness to external disturbances and flexibility in parameter settings.
arXiv:2508. 21172v3 Announce Type: replace-cross Abstract: Echo State Networks (ESNs) are a particular type of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) framework, popular for their fast and efficient learning.
arXiv:2606. 24969v1 Announce Type: new Abstract: While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balance efficient training with highly expressive latent states.
arXiv:2509. 24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal dependencies across ever-growing sequences?
arXiv:2606. 28380v1 Announce Type: cross Abstract: The intricate structures of biological neural networks largely emerge during development, guided by a comparatively compressed blueprint encoded in the genome.
arXiv:2607. 02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs.
arXiv:2602. 01196v2 Announce Type: replace Abstract: Recurrent neural policies are widely used in partially observable control and meta-RL tasks.
arXiv:2607. 24420v1 Announce Type: cross Abstract: Reservoir computing has emerged as an efficient machine learning framework for predicting time series generated by dynamical systems.
The paper studies how reservoir‑computing associative memories can reliably recall dynamical memories even when their basins of attraction are riddled and unpredictable. It finds that these basins have an octopus‑like shape, with a robust head near the attractor and thin, intertwined tentacles that span state space. By using cue‑driven generalized synchronization, the system bypasses the unpredictable tentacular regions and is driven into the robust basin head, establishing a quantitative link between cue duration, synchronization rate, and basin‑head radius.
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.
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:2506. 05678v3 Announce Type: replace Abstract: The evolution of sequence modeling architectures, from recurrent neural networks and convolutional models to Transformers and structured state-space models, reflects ongoing efforts to address the diverse temporal dependencies inherent in sequential data.
Dynamic Compression in Recurrent Networks proposes a method that lets recurrent models revisit and revise their fixed-size state through additional updates, rather than compressing all information in a single causal pass. This approach allows the model to retain lower-fidelity history and refine only the relevant parts when needed, reducing the required state size for accurate task reuse. Experiments show that dynamic compression lowers the recurrent state needed and scales better as the number of stored functions increases.