Neuro
Vision™
The first computational platform for modeling brain connectivity as simplicial complexes and hypergraphs. Decode higher-order neural interactions invisible to traditional methods.
Beyond Pairwise Connectivity
Traditional neuroscience tools analyze brain regions in pairs. NeuroVision reveals the hidden higher-order interactions where 3, 4, or more regions encode information collectively.
See the Invisible
Detect synergistic neural assemblies that carry 67% more task-relevant information than any pairwise combination.
Topological Precision
Persistent homology reveals 5 critical topological phase transitions across the human lifespan.
Real-Time Decoding
DSSNN architecture classifies 11 brain states from EEG in under 23ms with 97.3% accuracy.
Core Modules
Simplicial Complex Engine
Constructs Vietoris-Rips and Cech complexes from brain connectivity matrices. Computes persistent homology via Ripser++ with GPU acceleration.
O-Information Pipeline
Batch-parallel computation of O-information for all subsets up to order k=6. Distinguishes synergy from redundancy in neural assemblies.
Hypergraph Processor
Tensor-based hypergraph Laplacian computation with spectral decomposition. Enables multi-region co-fluctuation detection in real time.
DSSNN Classifier
Directed Semi-Simplicial Neural Network for brain state classification. Processes directed k-simplices with attention-based message passing.
Connectome Store
Optimized storage and retrieval of whole-brain connectomes with support for HCP, UK Biobank, and custom datasets up to 500 regions.
Validation Suite
Automated statistical testing, permutation analysis, and cross-validation frameworks for rigorous hypothesis testing on higher-order structures.
System Architecture
End-to-end pipeline from raw neural recordings to higher-order interaction discovery and brain state classification.
Data Ingestion
Preprocessing
Higher-Order Analysis
Machine Learning
Visualization & Export
How It Works
Upload Neural Data
Import fMRI, EEG, MEG, or Neuropixels recordings in any standard format. BIDS-compliant with automatic validation.
Configure Pipeline
Select analysis modules: persistent homology, O-information, hypergraph spectral, or DSSNN classification. Set parameters via GUI or API.
GPU-Accelerated Processing
Custom CUDA kernels process all higher-order subsets in parallel. 200-region connectome analyzed in 4.2 minutes on A100.
Interactive Visualization
Explore results through 3D brain renderings, topological landscapes, and interactive charts. Export publication-ready figures.
Benchmarks
Interactive performance metrics. Hover over data points for detailed values.
How We Compare
| Feature | NeuroVision™ | SPM | FSL | MNE |
|---|---|---|---|---|
| Higher-Order Interactions (k>2) | ● | ○ | ○ | ○ |
| Persistent Homology | ● | ○ | ○ | ○ |
| O-Information Decomposition | ● | ○ | ○ | ○ |
| Hypergraph Signal Processing | ● | ○ | ○ | ○ |
| DSSNN Brain State Classification | ● | ○ | ○ | ○ |
| GPU-Accelerated (CUDA/JAX) | ● | ○ | ● | ○ |
| Real-Time Streaming API | ● | ○ | ○ | ● |
| Multi-Modal Support | ● | ● | ● | ● |
| 3D Brain Visualization | ● | ● | ● | ● |
| Publication-Ready Figures | ● | ● | ● | ● |
Technical Specs
Input Formats
Computation
Output
Developer-First API
Build on top of NeuroVision with our comprehensive REST API and SDKs for Python, R, and MATLAB.
Secure Authentication
OAuth 2.0 + API keys with fine-grained permissions
WebSocket Streaming
Real-time data streaming for BCI and live EEG analysis
Docker Deployment
Single-command deployment with GPU passthrough
SDK Libraries
pip install neurovision for Python, CRAN for R, toolbox for MATLAB
import neurovision as nv
# Initialize with GPU acceleration
client = nv.Client(gpu=True, device="cuda:0")
# Load connectome data
conn = client.load_connectome(
"path/to/fmri_data.nii.gz",
atlas="schaefer_200",
format="bids"
)
# Compute O-information up to order k=5
oi = client.compute_o_information(
conn,
max_order=5,
method="knn",
parallel=True
)
# Build simplicial complex
sc = client.build_simplicial_complex(
conn,
method="vietoris_rips",
max_dim=3
)
# Persistent homology
ph = client.persistent_homology(sc)
# Brain state classification with DSSNN
model = nv.DSSNN.from_pretrained("eeg-11class")
states = model.predict(eeg_epochs)
# Export results
client.export(oi, format="hdf5")
client.visualize_3d(ph, output="brain.html")Use Cases
Neurodegeneration Research
Track topological biomarkers across Alzheimer's and Parkinson's disease progression with persistent homology analysis of longitudinal connectomes.
Brain-Computer Interfaces
Real-time brain state classification from EEG/ECoG using DSSNN for next-generation prosthetic control and communication systems.
Cognitive Neuroscience
Quantify synergy and redundancy in working memory, attention, and decision-making circuits using O-information decomposition.
Drug Discovery
Characterize pharmacological effects on higher-order neural connectivity patterns for targeted neurotherapeutic development.
Sleep Research
Map higher-order connectivity changes across sleep stages. Identify micro-arousal signatures invisible to spectral analysis.
Developmental Neuroscience
Track the emergence of higher-order interactions during brain development. Identify topological milestones from infancy to adulthood.
What Researchers Say
NeuroVision uncovered synergistic assemblies in our working memory data that we had completely missed with traditional graph analysis. It changed how we think about prefrontal function.
The 400x GPU speedup in O-information computation made population-level higher-order analysis feasible for the first time. We processed the entire UK Biobank cohort in under a week.
DSSNN outperformed every model we benchmarked for BCI decoding. The directed simplicial approach captures temporal dynamics that graph neural networks completely miss.
Ready to See Beyond
Pairwise?
Join 200+ research labs using NeuroVision to decode higher-order neural connectivity. Free academic licenses available.