arXiv AI
Beyond Episodic AI: Cognitive Field Networks for Biologically Inspired Persistent Cognition
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The Topological Trouble With Transformers
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CogniFold: Always-On Proactive Memory via Cognitive Folding
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Memory in Deep Time-Series Models
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Constructed Reality, Contested Priors: Decoupling and the Architecture of Cognitive Relapse Under the Free Energy Principle
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Metaplasticity as adaptive gradient preconditioning for incremental learning
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ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks
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.