Aims & Scope
International Journal of Neuroinformatics (IJNN) publishes computational methods, algorithms, and data science innovations that advance our understanding of neural systems through informatics approaches. We focus on the development and application of computational tools, databases, and analytical frameworks for neuroscience research.
Core Research Domains
Computational Neuroscience
Mathematical and computational modeling of neural systems, from single neurons to large-scale brain networks.
- Neural network modeling and simulation
- Computational models of neural circuits
- Biophysical modeling of neurons and synapses
- Dynamical systems analysis of neural activity
- Theoretical frameworks for neural computation
- Spike train analysis and neural coding
"A novel spiking neural network model for temporal pattern recognition with biologically plausible learning rules"
Brain Imaging Informatics
Computational methods for acquiring, processing, analyzing, and visualizing neuroimaging data across multiple modalities.
- fMRI data analysis algorithms and pipelines
- Structural MRI processing and segmentation
- Diffusion tensor imaging (DTI) analysis
- EEG/MEG signal processing and source localization
- Multi-modal imaging integration methods
- Image registration and atlas construction
"A machine learning pipeline for automated brain lesion segmentation in multi-contrast MRI sequences"
Neuroinformatics Infrastructure
Development of databases, software tools, standards, and platforms for managing and sharing neuroscience data.
- Neuroscience databases and data repositories
- Data standardization and ontology development
- Software tools for neural data analysis
- Workflow systems for neuroscience research
- Data sharing platforms and protocols
- Metadata frameworks for neural datasets
"An open-source platform for standardized storage and retrieval of electrophysiology recordings"
Machine Learning for Neuroscience
Application of artificial intelligence and machine learning techniques to analyze neural data and decode brain function.
- Deep learning for neuroimaging analysis
- Neural decoding and brain state classification
- Dimensionality reduction for neural data
- Graph neural networks for brain connectivity
- Unsupervised learning for neural pattern discovery
- Transfer learning across neuroimaging datasets
"A convolutional neural network architecture for predicting cognitive states from fMRI time series"
Secondary Focus Areas
Systems Neuroscience Informatics
Computational approaches to understanding neural circuits and systems-level brain organization, including connectomics and network neuroscience.
Brain-Computer Interface Algorithms
Signal processing and machine learning algorithms for decoding neural signals in BCI applications, focusing on computational methodology rather than device engineering.
Cognitive Informatics
Computational models of cognitive processes, including attention, memory, decision-making, and language processing based on neural data.
Neurophysiological Signal Analysis
Advanced signal processing techniques for analyzing EEG, local field potentials, calcium imaging, and other electrophysiological recordings.
Molecular Neuroinformatics
Computational analysis of molecular and genetic data in neuroscience, including transcriptomics, proteomics, and single-cell sequencing of neural tissues.
Neuroimage Computing
Novel algorithms for medical image computing specific to neurological applications, including reconstruction, enhancement, and quantitative analysis methods.
Emerging Research Frontiers
Artificial Intelligence-Driven Discovery
Novel AI architectures and algorithms that enable automated hypothesis generation and discovery from large-scale neuroscience datasets.
Interpretable Neural Networks
Methods for explaining and interpreting deep learning models applied to neural data, bridging machine learning and neuroscience understanding.
Real-Time Neural Data Processing
Algorithms and systems for online analysis of neural signals with applications in closed-loop experiments and adaptive neurotechnologies.
Federated Neuroinformatics
Distributed computing frameworks and privacy-preserving methods for collaborative analysis across multiple neuroscience datasets and institutions.
Note: Submissions in emerging areas undergo additional editorial review to ensure substantial methodological contribution and alignment with journal scope. Preliminary or purely speculative work may be redirected.
Article Types & Editorial Priorities
Fast-Track Review
Standard Review
Selective Acceptance
Editorial Standards & Requirements
Reporting Guidelines
Authors must follow discipline-specific reporting standards: STROBE for observational studies, CONSORT for trials, PRISMA for systematic reviews, and ARRIVE for animal research.
Data Availability
Code and data supporting computational findings must be made available in public repositories. Proprietary datasets require detailed methodology for reproducibility assessment.
Ethics Compliance
Human subject research requires IRB approval. Animal studies must comply with institutional guidelines. Data reuse must respect original consent and privacy regulations.
Preprint Policy
Preprints on arXiv, bioRxiv, or similar servers are welcomed. Authors must disclose preprint DOI at submission. Preprint posting does not affect consideration.
Software Standards
Software tools must include documentation, version control, and open-source licensing when possible. Benchmark comparisons against existing methods are required.
Statistical Rigor
Appropriate statistical methods, multiple comparison corrections, and validation strategies must be employed. Cross-validation and independent test sets are expected for ML studies.
Publication Metrics
Average First Decision
Acceptance Rate
Time to Publication
Access Model
Questions about scope? Authors uncertain about fit are encouraged to submit a pre-submission inquiry with title, abstract, and key methods to [email protected]. Editorial team responds within 5 business days.
