arXiv Computer Vision
QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex
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CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data
arXiv:2604. 04958v3 Announce Type: replace-cross Abstract: Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis.
arXiv AI
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CAPT: A Multi-task Continuous Autoregressive Transformer enabling Cross-dataset and Cross-species Transfer for Calcium Population Dynamics
arXiv:2607. 23258v1 Announce Type: new Abstract: Large-scale calcium imaging has created an opportunity to build foundation-style models for neural population dynamics, but a central question remains unresolved: \textbf{whether a model pretrained on one collection of recordings can generalize to new datasets, experimental paradigms, and even species.
Hugging Face Trending Papers
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Leveraging unlabelled data for generalizable neural population decoding
Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has shown that tokenizing neural data at the spike level facilitates multi-session pretraining and delivers state-of-the-art decoding performance.
arXiv Machine Learning
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Leveraging unlabelled data for generalizable neural population decoding
arXiv:2607. 14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments.
arXiv AI
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Next-Token Prediction Learns Generalisable Representations of Sleep Physiology
arXiv:2606. 09605v1 Announce Type: new Abstract: Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains.
arXiv AI
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EEGDancer: Dynamic Emotion Latent Space Masked Modeling with Reinforcement Learning for EEG Continuous Emotion Prediction
arXiv:2606. 05855v1 Announce Type: cross Abstract: Continuous electroencephalography (EEG) emotion prediction aims to model the temporal evolution of human emotional states from EEG signals.