Machine learning to decode brain signals, analyze neuroimaging, and model the nervous system.
59 papers in this topic (ICML 2026).
Amrith Lotlikar, Ian Christopher Tanoh, Praful K. Vasireddy, Andrew Lanpouthakoun +4
Multi-compartment Hodgkin–Huxley (HH) models provide a principled framework for predicting neural dynamics and responses to electrical stimulation. However, fitting HH biophysical…
SUBBA REDDY OOTA, Vijay Rowtula, Satya Sai Srinath Namburi GNVV, Khushbu Pahwa +4
Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains or which…
Yizhuo Lu, Changde Du, Qingyu Shi, Hang Chen +4
Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain-Computer Interfaces (BCIs). A major limitation of prior…
Haowei Xu, Yixin Chen, Wanyi Fu, Hongbin Han +1
Neuromodulation can be viewed as closed-loop control of high-dimensional spatiotemporal fields on irregular 3D morphologies, coupling membrane electrophysiology with ionic…
Lingyuan Meng, KE LIANG, Hao Li, Meng Liu +4
Multi-modal brain network analysis aims to predict neuropsychiatric status from functional connectomes with heterogeneous phenotypes. However, most existing methods treat…
Chenggang Chen, Zhiyu Yang, Xiaoqin Wang
Deep neural networks currently provide the leading quantitative models of neural responses in sensory systems. However, these networks remain implausible as models of sensory…
Michael Ibrahim, Hanqi Zhao, Eli Zachary Sennesh, Zhi Li +4
Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two…
Emily Cheng, Aditya R. Vaidya, Richard Antonello
Research has repeatedly demonstrated that intermediate hidden states extracted from large language models and speech audio models predict measured brain response to natural…
Haoyu Albert Wang, Wei P Dai, Jialun Ma, Jiawei Zhang +4
Lateral connections (LCs) are ubiquitous in the cortical circuits. While DL architectures have rich intralayer interactions to support feature selectivity and contextual…
Dongxun Jiang, Borui Jia, Yuxuan Wang, Dongdong Zhang
Graph-based attention deficit hyperactivity disorder (ADHD) detection methods have been extensively studied, but comparatively less attention has been paid to short-term brain…
Guobin Shen, Dongcheng Zhao, Yiting Dong, Qian Zhang +1
Artificial and biological systems may converge on similar computational strategies despite different architectures and learning mechanisms—a form of convergent evolution. We test…
Dongyang Ma, Zhengyu Ma, Yifan Huang, Chenlin Zhou +2
Retina-like event cameras and brain-inspired Spiking Neural Networks (SNNs) demonstrate exceptional energy efficiency through bio-inspired sensing and computation. While SNNs are…
Sunghwan Lee, Jihun Kim, Chae Lynn Kim, Ji Yun Park +1
Decoding fMRI into natural language is challenging because strong, pre-trained language priors can dominate autoregressive generation, obscuring whether a model truly utilizes…
Haitao Wu, Qirui Zhang, Zhouheng Yao, Shangquan Sun +4
Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscience. However, existing…
Xinhong Xu, Yimeng Zhang, Qichen Qian, Yuanlong Zhang
Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis. However, for functional calcium traces, existing approaches…
Byungwoo Kang, Maceo Richards, Bernardo L. Sabatini
Credit assignment, the process of determining how changes in individual neurons and synapses influence a network’s output, is central to learning in brains and machines. Noise…
Mohammad Hosseini, Eray Erturk, Saba Hashemi, Maryam M. Shanechi
Large-scale, multi-subject widefield calcium imaging provides unprecedented access to brain-wide cortical dynamics. However, the high dimensionality, complex spatiotemporal…
Ganchao Wei, Daniela F De Albuquerque, Miles Martinez, Shiyang Pan +1
While neuroscience experiments have repeatedly demonstrated the involvement of large populations of neurons in even simple behaviors, these studies have just as often reported…
Ezekiel Williams, Alexandre Payeur, Guillaume Lajoie
Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information that can plausibly be used during…
Runhe Zhou, Shanglin Li, Guanxiang Huang, Xinliang Zhou +4
Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical…
Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu +1
Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific…
Ziyu Jia, Junyi Lin, Pu Wan, Jinxin Pi +4
Electroencephalography (EEG) foundation models (EFMs) have achieved strong performance across a wide range of downstream EEG tasks via pretraining and fine-tuning. Through…
Michela Proietti, Roberto Capobianco, Mariya Toneva
Understanding the alignment between large language models (LLMs) and human brain activity can reveal computational principles underlying language processing. This work describes a…
Nicholas Blauch, George A. Alvarez, Talia Konkle
Human vision is foveated, with variable resolution peaking at the center of a large field of view; this reflects an efficient trade-off for active sensing, allowing eye-movements…
Junfeng Zuo, Yuhang He, Wenhao Zhang, Fang Fang +1
Navigation in complex environments relies on internal spatial representations that guide action. While the brain employs a diverse repertoire of spatial tuning cells—including…
Subati Abulikemu, Tiago Azevedo, Michail Mamalakis, John Suckling
In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties…
Wei Wang, Fang He, Yifan Li, Wanying Qu +3
Existing EEG models are limited by electrode heterogeneity and rigid "channel-first" architectures that treat sensors as independent features. We propose Brain Signal Rendering…
Shuqi Zhu, Yi Zhong, Ziyi Ye, Bangde Du +3
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations…
Timothy Doyeon Kim, Ulises Pereira Obilinovic, Yiliu Wang, Eric Todd SheaBrown +1
Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity structures can generate identical…
Ganxi Xu, Zhao-Rong Lai, Yuting Tang, Yonghao Song +4
Visual prostheses hold great promise for restoring vision in blind individuals. While researchers have successfully utilized M/EEG signals to evoke visual perceptions during the…
Haoning Wang, Wenchao Yang, Shuai Shen, Yang Li
While EEG foundation models have shown significant potential in universal neural decoding across tasks, their advancement remains constrained by the inadequacy modeling of…
Christian Nauck, Junyou Zhu, Michael Lindner, Frank Hellmann
The robustness of synchronization is typically characterized by scalar, per-node stability indices whose dependence on topology is studied via network science or graph neural…
Jaeyoon Sim, Soojin Hwang, Seunghun Baek, Guorong Wu +1
Understanding complex interactions between brain regions is critical for early neurodegenerative disease classification such as Alzheimer’s Disease (AD) and Parkinson’s Disease…
Yicheng Feng, Hairong Chen, Ziyu Jia, Samir Bhatt +1
Alzheimer’s disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important…
Yifan Wang, Yijia Ma, Wen Li, Chenyu You
High-fidelity EEG generation is critical for alleviating data scarcity and addressing privacy constraints in large-scale neural modeling. Despite recent progress, most existing…
Dulhan Jayalath, Oiwi Parker Jones
Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient generalisation by…
Yamin Li, Shiyu Wang, Chang Li, Ange Lou +4
Functional magnetic resonance imaging (fMRI) provides dynamic measurements of human brain activity at high spatial resolution and depth, but its use is constrained by high cost,…
Noga Mudrik, Yuxi Chen, Gal Mishne, Adam Shabti Charles
Many fields collect large-scale temporal data through repeated measurements (`trials’), where each trial is labeled with a set of metadata variables spanning several categories.…
Abdulkadir Gokce, Yingtian Tang, Martin Schrimpf
Task-optimized neural networks are the leading in-silico models of sensory cortex, yet the field lacks a unified understanding of which modeling choices drive improved brain…
Sijin Yu, Zijiao Chen, Zhenyu Yang, Zihao Tan +4
Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically faithful surface-based models. We argue this is partly driven by…
Hui Zheng, Haiteng Wang
Foundation models for intracranial neural recordings aim to learn generalizable representations from large-scale unlabeled data. However, existing approaches rely on suboptimal…
Mo Wang, Wenhao Ye, Junfeng Xia, Junxiang Zhang +4
Self-supervised fMRI foundation models have shown promising transfer performance, yet most rely on predefined region-level parcellations that discard fine-grained voxel…
Francesco Innocenti, El Mehdi Achour, Rafal Bogacz
Predictive coding (PC) is a biologically plausible alternative to standard backpropagation (BP) that minimises an energy function with respect to network activities before…
Jiaming Zhuo, Shuai Zhai, Ziyi Ma, Kun Fu +4
Brain network classification is pivotal for diagnosing neurological disorders, yet identifying interpretable functional biomarkers fundamentally relies on precise parcellation.…
KIEREN YU, Ziyang LIU, Chang Huang, Kaishun Wu
EEG foundation models aim to learn transferable representations, yet EEG recordings are dominated by high-frequency noise and large cross-subject variability. Existing pretraining…
Xiran Chen, Xiaoke Yang, Jian Zhou, Zhao Lv +1
Auditory attention decoding (AAD) based on Electroencephalography (EEG) aims to identify the attended speaker in multi-speaker environments. However, existing methods typically…
Kaiwen Zha, Chao Li, Hao He, Peng Cao +4
This paper introduces a novel cross-physiology translation task: synthesizing sleep electroencephalography (EEG) from respiration signals. To address the significant complexity…
Jinhan Liu, Mahsa Shoaran
Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time, challenging robust representation learning from…
Haorui Sun, Ardyn Vivienne Olszko, Niharika Singh, David C. Jangraw
We present Reading Observed At Mindless Moments (ROAMM), a multimodal dataset comprising 50 hours of simultaneous EEG and eye-tracking recordings collected during naturalistic…
Connor Lane, Mihir Tripathy, Leema Krishna Murali, Ratna Sagari Grandhi +4
We study the problem of training self-supervised foundation models for functional MRI. Our main contributions are: (1) we introduce a new model family (CortexMAE) trained using…
Jingjing Hu, Dan Guo, Haofan Cheng, Zeng ying +3
Despite the high accuracy of EEG-based emotion recognition, existing models remain opaque "black boxes", lacking semantic grounding between abstract neural features and…
Jiayu Lu, Yujin Wang, Xiaofeng Liu, Dandan Li +1
Functional brain network analysis plays an important role in understanding and diagnosing psychiatric disorders. However, current methods struggle with subject variations,…
Yongzhi She, Qihua Zhou, Yuhao Wang, Yaodong Huang +2
Recently, the spiking neural networks (SNNs) have shown great promise in enhancing AI task performance by utilizing the brain-inspired and energy-efficient computational paradigm…
Tianqiu Zhang, Muyang Lyu, Xiao Liu, Si Wu
Humans abstract experiences into structured representations to facilitate pattern inference and knowledge transfer. While the hippocampal-entorhinal (HPC-MEC) circuit is known to…
Hongyu Shi, Kaizhong Zheng, Wensheng Zhai, Shuai Jiang +2
Multi-modal neuroimaging-based psychiatric diagnosis must integrate cross-modal agreement with modality-specific complementarity, yet in real multi-site cohorts these signals are…
Jack Goffinet, Casey Hanks, David Carlson
Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication across brain regions.…
David Aaron Koplow, Tomaso Poggio, Liu Ziyin
Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized by homeostatic constraints.…
Tianhao Huang, Guanghui Min, Zhenyu Lei, Aiying Zhang +1
Unraveling how macroscopic cognitive phenotypes emerge from microscopic neuronal connectivity remains one of the core pursuits of neuroscience. To this end, researchers typically…
Minxu Liu, Donghai Guan, Chuhang Zheng, Chunwei Tian +2
Understanding and decoding brain activity into visual representations is a fundamental challenge at the intersection of neuroscience and artificial intelligence. While…
Metadata from BioTender-max/icml2026-ai-bio (CC0-1.0).