arXiv:2611.06932v1 Announce Type: cross Abstract: Oscillatory phase coding (OPC) is widely observed in neural systems and has been implicated in cognitive functions such as memory and sequence proces...
arXiv:2606. 00732v1 Announce Type: new Abstract: Learning long-range non-stationary temporal patterns remains a core challenge for modern sequence models, particularly in strict streaming settings.
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:2507.13638v4 Announce Type: replace-cross Abstract: A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive...
arXiv:2609.08070v1 Announce Type: cross Abstract: The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timin...
As new evidence arrives, a sequence model must update what it remembers and how memory influences predictions. While Transformers incur computation and cache costs scaling with context length, fixed-s...
NeurDuo-EEG is a causal EEG foundation model that introduces channel‑resolved persistent memory and multi‑timescale memory management, allowing it to model continuous EEG with a fixed‑size state. Trained on 3,955 hours of EEG from 17 public datasets, it outperforms baselines on most short‑window and long‑sequence tasks, notably improving seizure detection AUC‑PR from 0.285 to 0.471. The Small variant achieves these gains with only 4.7 M parameters and supports efficient streaming inference with constant per‑chunk latency even as history grows to one hour.
The paper investigates how information-theoretic and geometric compression unfolds over time in recurrent neural networks, specifically discretized LSTMs. It finds that compression is strongest at the final timesteps of many datasets and attributes this to three mechanisms: class separability, temporal stationarity of shared dynamics, and limited representational capacity. Through synthetic and real-world experiments, the authors demonstrate that these factors jointly determine the extent of temporal compression and its role as a regularizer when excess capacity exists, but can harm generalization when it consumes task-relevant memory.
arXiv:2608.29304v1 Announce Type: new Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...
The paper introduces the Progressive Memory Transformer (PMT), a transformer variant that adds writable, window‑aligned memory to expose mid‑range representations alongside token and sequence‑level outputs. PMT is trained with a hierarchical learning framework that applies separate objectives at local, mid‑range, and global scales, encouraging the model to capture fine‑grained variation, window‑level motifs, and overall sequence agreement. Experiments on seven UCR/UEA/UCI classification datasets, a cue‑retention probe, and forecasting tasks show that PMT achieves strong low‑label classification performance, competitive multi‑horizon forecasting, and evidence that its memory states encode mid‑range motifs.
arXiv:2608.22215v1 Announce Type: new Abstract: Large language model (LLM) agents operate in dynamic environments where knowledge continuously evolves. Existing memory systems typically treat externa...
arXiv:2607. 22575v1 Announce Type: new Abstract: Human episodic memory supports the retrieval of experiences that unfold over extended timescales, yet the computational mechanisms underlying this ability remain debated due to the limited mechanistic accessibility in long-term memory experiments in humans.