arXiv Computer Vision

QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex

arXiv AI

Jun 2

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.

By Xinhong Xu, Yimeng Zhang, Qichen Qian, Yuanlong Zhang

llms nlp

arXiv AI

Jul 28

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.

By Xinhong Xu, Yimeng Zhang, Yuanlong Zhang

llms rag nlp multimodal

Hugging Face Trending Papers

Jul 15

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.

fine-tuning benchmarks

arXiv Machine Learning

Jul 16

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.

By Ximeng Mao, Nanda H. Krishna, Avery Hee-Woon Ryoo, Matthew G. Perich, Guillaume Lajoie

fine-tuning benchmarks

arXiv AI

Jun 9

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.

By Jonathan F. Carter, Lionel Tarassenko

llms rag efficiency multimodal benchmarks

arXiv AI

Jun 6

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.

By Zhihao Zhou, Weishan Ye, Li Zhang, Gan Huang, Zhen Liang

llms diffusion reinforcement-learning efficiency benchmarks

arXiv AI

Jun 16

OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens

arXiv:2604. 18827v2 Announce Type: replace-cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision.

By Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert, Goirik Chakrabarty, Hasan A. Bedel, Paul G. Fahey, Yongrong Qiu, Marissa A. Weis, Michaela Vystr\v{c}ilov\'a, Taliah Muhammad, Lydia Ntanavara, Rachel E. Froebe, Kayla Ponder, Zheng Huan Tan, Emin Orhan, Erick Cobos, Sophia Sanborn, Katrin Franke, Fabian H. Sinz, Alexander S. Ecker, Andreas S. Tolias

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 15

SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting

arXiv:2606. 13901v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics.

By Jafar Bakhshaliyev, Niels Landwehr

rag computer-vision robotics benchmarks

arXiv AI

Jul 2

Learning When to Listen: Gated Affect Fusion for Human Motion Prediction

arXiv:2607. 00296v1 Announce Type: cross Abstract: Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy multimodal observations.

By Jingni Huang

llms multimodal

arXiv Machine Learning

Jul 22

Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning

arXiv:2607. 18923v1 Announce Type: new Abstract: ECoG-based visual semantic decoding enables inference of semantic interpretation of visual perception from complex, noisy brain activity.

By Stella Ho, Joel Villalobos, Joseph West, Jingyang Liu, Weijie Qi, Haruhiko Kishima, Ryohei Fukuma, Takufumi Yanagisawa, Sam E. John, David B. Grayden

llms

arXiv Machine Learning

Jun 30

BrainJanus: A Unified Model for Understanding and Generation across Brain, Vision, and Language

arXiv:2606. 30319v1 Announce Type: cross Abstract: Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscience.

By Haitao Wu, Qirui Zhang, Zhouheng Yao, Shangquan Sun, Qihao Zheng, Mianxin Liu, Chi Zhang, Wanli Ouyang, Chunfeng Song, Changqing Zhang, Jiamin Wu

nlp multimodal benchmarks safety

arXiv Machine Learning

Jun 11

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.

By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han

llms benchmarks

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