About the International Journal of Neuroinformatics
Advancing computational neuroscience through innovative algorithms, data structures, and analytical frameworks that decode the complexity of neural systems.
IJNN publishes original research, methodological reviews, and software papers addressing the computational challenges inherent in neuroscience data. Our scope encompasses algorithm development, data architecture design, and analytical tool creation across multiple scales of neural organization.
Neural Data Structures & Databases
Schemas for electrophysiology, imaging, behavioral, and omics data; database architectures; data standardization frameworks; metadata ontologies; querying systems for large-scale neural repositories.
Algorithm Development
Signal processing for neural recordings; spike sorting algorithms; image segmentation for microscopy; connectivity analysis; network graph algorithms; time-series analysis methods; dimensionality reduction techniques.
Machine Learning & AI
Supervised and unsupervised learning for neural classification; deep learning architectures for brain imaging; reinforcement learning models; neural network interpretability; transfer learning across datasets; feature extraction pipelines.
Software Tools & Platforms
Analysis toolboxes; visualization software; workflow management systems; cloud computing frameworks; reproducible research platforms; API development; open-source library contributions.
Data Integration & Sharing
Multi-modal data fusion methods; cross-species data alignment; data harmonization protocols; interoperability standards; collaborative data sharing platforms; FAIR data principles implementation.
Computational Modeling
Simulation frameworks; biophysical models; network dynamics algorithms; parameter estimation methods; model validation techniques; computational efficiency optimization.
IJNN emphasizes computational rigor, reproducibility, and practical utility. We seek manuscripts that advance the technical infrastructure of neuroscience through well-validated algorithms, scalable software architectures, and data analysis innovations.
Algorithmic Contributions
- Novel computational methods with theoretical foundations
- Complexity analysis and performance benchmarking
- Comparative evaluations against existing approaches
- Validation on synthetic and real neural datasets
- Scalability demonstrations for large-scale data
Software & Tool Papers
- Open-source code availability (GitHub, GitLab, etc.)
- Comprehensive documentation and tutorials
- Installation instructions and dependency management
- Use case demonstrations with sample datasets
- Performance metrics and computational requirements
Scope Clarification: IJNN focuses exclusively on computational methods, algorithms, and data science tools for neuroscience. We welcome studies that apply these approaches to experimental or clinical neural datasets, provided the primary contribution is methodological or informatics-driven.
IJNN accommodates diverse contribution types that advance neuroinformatics methodology:
- Research Articles: Original algorithms, data structures, or analytical frameworks with rigorous validation
- Software Papers: Novel tools, libraries, or platforms with code availability and performance evaluation
- Methodological Reviews: Comprehensive surveys of computational approaches within specific neuroinformatics domains
- Data Descriptors: Large-scale neural datasets with standardized formats, accessibility documentation, and reuse potential
- Short Communications: Preliminary algorithms, workflow innovations, or computational insights warranting rapid dissemination
- Conference Proceedings: Selected computational papers from neuroinformatics conferences with extended validation
All submissions undergo rigorous peer review by computational experts evaluating algorithmic soundness, code quality, reproducibility, and impact potential. IJNN employs single-blind review as standard, with double-blind options available upon request.
Technical Requirements:
Code submissions should include: (1) version-controlled repositories with clear licensing, (2) README files with installation and execution instructions, (3) sample datasets or synthetic data generators, (4) computational environment specifications (dependencies, versions, hardware), and (5) performance benchmarks where applicable.
Algorithms must provide: (1) mathematical formulations, (2) pseudocode or flowcharts, (3) complexity analysis, (4) parameter sensitivity assessments, and (5) comparisons against baseline methods using standard evaluation metrics.
IJNN publishes under Creative Commons Attribution 4.0 (CC BY 4.0), ensuring unrestricted access to computational methodologies worldwide. Authors retain copyright while enabling broad reuse of algorithms, code, and datasets.
We strongly encourage:
- Deposition of source code in public repositories (GitHub, Zenodo, Figshare)
- Archiving of benchmark datasets in domain-specific or general-purpose repositories
- Inclusion of Jupyter notebooks or executable workflows demonstrating methods
- ORCID identifiers for all authors to enhance attribution and discoverability
- Preprint posting on bioRxiv, arXiv, or institutional repositories prior to submission
IJNN's editorial board comprises computational neuroscientists, bioinformaticians, machine learning specialists, and software engineers from leading research institutions worldwide. Our editors provide expert guidance on algorithm design, software architecture, and data science best practices throughout the publication process.
The journal adheres to COPE ethical guidelines, ensuring transparent handling of authorship disputes, data integrity concerns, and potential conflicts of interest. We maintain rigorous standards for reproducibility, requiring sufficient methodological detail for independent replication of computational results.
Authors submit manuscripts through our online submission portal, including all supplementary files (code, datasets, documentation). The editorial office acknowledges receipt within 48 hours and assigns manuscripts to associate editors based on computational expertise.
Typical Timeline:
- Initial editorial assessment: 3-5 business days
- Peer review (two or more reviewers): 2-4 weeks
- Author revision: As needed (typically 2-4 weeks)
- Final decision: 1 week post-revision submission
- Online publication: 48 hours after acceptance
Detailed formatting requirements and submission guidelines are available in our Instructions for Authors. For questions about scope, methodology, or technical requirements, contact our editorial office at [email protected].
Contribute Your Computational Innovation
Join the global neuroinformatics community in developing the algorithmic foundations of brain science. Submit your novel methods, software tools, or data resources to advance computational neuroscience worldwide.
