Decoupling model descriptions from execution: a modular paradigm for extensible neurosimulation with EDEN [0.03%]
解耦模型描述和执行：使用EDEN进行可扩展神经模拟的模块化范式
Sotirios Panagiotou,Rene Miedema,Dimitrios Soudris et al.
Sotirios Panagiotou et al.
Computational-neuroscience simulators have traditionally been constrained by tightly coupled simulation engines and modeling languages, limiting their flexibility and scalability. Retrofitting these platforms to accommodate new backends is ...
A neuronal imaging dataset for deep learning in the reconstruction of single-neuron axons [0.03%]
用于重建单神经元轴突的深度学习的神经影像数据集
Liya Li,Ying Hu,Xiaojun Wang et al.
Liya Li et al.
Neuron reconstruction is a critical step in quantifying neuronal structures from imaging data. Advances in molecular labeling techniques and optical imaging technologies have spurred extensive research into the patterns of long-range neuron...
Correction: Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression [0.03%]
订正：来自自我参照判断的自我图式的跨诊断聚类确定了健康人格和抑郁亚型
Geoffrey Chern-Yee Tan,Ziying Wang,Ethel Siew Ee Tan et al.
Geoffrey Chern-Yee Tan et al.
[This corrects the article DOI: 10.3389/fninf.2023.1244347.]. Keywords: clustering; depression; depression s...
Published Erratum
Frontiers in neuroinformatics. 2025 Jul 31:19:1633196. DOI:10.3389/fninf.2025.1633196 2025
Sensitivity analysis of a mathematical model of Alzheimer's disease progression unveils important causal pathways [0.03%]
敏感性分析揭示了阿尔茨海默病发展数学模型中的重要因果途径
Seyedadel Moravveji,Halima Sadia,Nicolas Doyon et al.
Seyedadel Moravveji et al.
Introduction: Mathematical models serve as essential tools to investigate brain aging, the onset of Alzheimer's disease (AD) and its progression. By studying the representation of the complex dynamics of brain aging proce...
Breaking barriers: broadening neuroscience education via cloud platforms and course-based undergraduate research [0.03%]
打破壁垒：利用云端平台和基于课程的本科生研究拓展神经科学教育
Franco Delogu,Chantol Aspinall,Kimberly Ray et al.
Franco Delogu et al.
This study demonstrates the effectiveness of integrating cloud computing platforms with Course-based Undergraduate Research Experiences (CUREs) to broaden access to neuroscience education. Over four consecutive spring semesters (2021-2024),...
Decoding event-related potentials: single-dose energy dietary supplement acts on earlier brain processes than we thought [0.03%]
解码事件相关电位：单剂量能量膳食补充剂的作用比我们想象的更早影响大脑过程
Karina J Maciejewska
Karina J Maciejewska
Introduction: This paper describes an experimental work using machine learning (ML) as a "decoding for interpretation" to understand the brain's physiology better. ...
Digitoids: a novel computational platform for mimicking oxygen-dependent firing of neurons in vitro [0.03%]
Digitoids：一种新颖的计算平台 用于模仿体外氧依赖性神经元放电行为
Rachele Fabbri,Ermes Botte,Arti Ahluwalia et al.
Rachele Fabbri et al.
Introduction: Computational models are valuable tools for understanding and studying a wide range of characteristics and mechanisms of the brain. Furthermore, they can also be exploited to explore biological neural networ...
From pronounced to imagined: improving speech decoding with multi-condition EEG data [0.03%]
从发音到想象：利用多条件EEG数据改善语音解码
Denise Alonso-Vázquez,Omar Mendoza-Montoya,Ricardo Caraza et al.
Denise Alonso-Vázquez et al.
Introduction: Imagined speech decoding using EEG holds promising applications for individuals with motor neuron diseases, although its performance remains limited due to small dataset sizes and the absence of sensory feed...
Bridging neuroscience and AI: a survey on large language models for neurological signal interpretation [0.03%]
神经科学与人工智能的桥梁：大规模语言模型在神经系统信号解释中的应用综述
Sreejith Chandrasekharan,Jisu Elsa Jacob
Sreejith Chandrasekharan
Electroencephalogram (EEG) signal analysis is important for the diagnosis of various neurological conditions. Traditional deep neural networks, such as convolutional networks, sequence-to-sequence networks, and hybrids of such neural networ...
Editorial: Advanced EEG analysis techniques for neurological disorders [0.03%]
专家观点：神经系统疾病的高级EEG分析技术
Jisu Elsa Jacob,Sreejith Chandrasekharan
Jisu Elsa Jacob