One platform for neurobioinformatics and computational neuroscience.
Neuron Lab is a research platform that unifies two fields that usually live in different toolchains: high-throughput neurobioinformatics (single-cell RNA-seq, GWAS, multi-omics, sequence analysis) and mechanistic computational neuroscience (biophysical neurons, spiking networks, plasticity, connectomes).
Every module runs behind the same tenant-isolated, reproducible interface — so a student running QC on a scRNA-seq dataset and a PI simulating an E/I network on the same platform share the same audit trail, storage, and export format.
What you can do here
Over 145 analyses across three surfaces, each with parameters that map directly to the underlying literature.
Neurobioinformatics
120+ analyses- Single-cell & bulk RNA-seq: QC, normalization, HVGs, PCA, clustering, DEGs, marker discovery
- GWAS & population genetics: association testing, LD, PCA, kinship, heritability
- Sequence & variant analysis: alignment stats, variant calling summaries, annotation
- Multi-omics integration, pathway enrichment, and network inference
- Foundation models for genomics and single-cell (embedding, cell-type inference, perturbation)
Computational Neuroscience
33+ models- Single-neuron models: Hodgkin–Huxley, LIF, AdEx, Izhikevich, FitzHugh–Nagumo, Morris–Lecar, Hindmarsh–Rose
- Spike-train statistics: ISI, CV, Fano factor, Poisson generators, autocorrelation, cross-correlation
- Network dynamics: Wilson–Cowan E/I, Kuramoto synchrony, ring attractors, balanced networks
- Plasticity & memory: STDP windows, Hebbian learning, Hopfield associative memory
- Connectome metrics: graph-theoretic measures, modularity, rich-club, small-worldness
Neuron Lab
Built-in- Project workspaces with dataset versioning and audit logs
- Multi-tenant auth with role-based access (undergraduate through PI)
- Reproducible analysis runs — inputs, parameters, and outputs captured together
- Export to CSV / JSON and downloadable figures for every module
- Foundation-model gateway with rate limits and usage transparency
Principles
Reproducibility first
Every run captures inputs, parameters, and code version. Results replay identically across machines and time.
Isolation by default
Row-level security scopes datasets, projects, and results to your account — no cross-tenant leakage, ever.
Composable modules
A shared analysis contract lets us add new domains and models without changing your workflow.
Domain-honest UX
Parameters mirror the terminology used in the underlying literature and reference libraries.
Deep, not shallow
Each analysis returns statistics, diagnostics, and plots — not just a single number.
Open foundations
Built on well-known algorithms (Scanpy-style QC, PLINK-style GWAS, NEST/Brian-style simulators) so results are auditable.
Who it's for
Neuron Lab is used by undergraduates learning the field, MSc and PhD students running thesis analyses, postdocs prototyping models, and PIs who need every figure in a paper to be re-runnable years later. The same workspace scales from a single seminar assignment to a multi-lab consortium.