| name | hierarchical-connectome-ssm |
| description | Parallelized Hierarchical Connectome (PHC) framework that upgrades temporal State-Space Models into spatiotemporal recurrent networks for brain connectivity modeling.. Activation: hierarchical connectome, state-space models, spatiotemporal. |
| version | 1.0.0 |
| author | Research Synthesis |
| license | MIT |
| metadata | {"hermes":{"tags":["hierarchical connectome","state-space models","spatiotemporal","spiking neural networks","brain connectivity","SSM"],"source_paper":"Parallelized Hierarchical Connectome: A Spatiotemporal Recurrent Framework for Spiking State-Space Models (arXiv:2604.01295v1)","published":"2026-04-01","category":"neuroscience"}} |
Hierarchical Connectome for Spiking State-Space Models
Overview
Parallelized Hierarchical Connectome (PHC) framework that upgrades temporal State-Space Models into spatiotemporal recurrent networks for brain connectivity modeling.
This skill is based on the research paper "Parallelized Hierarchical Connectome: A Spatiotemporal Recurrent Framework for Spiking State-Space Models" published on arXiv (2604.01295v1).
Activation Keywords
- hierarchical connectome
- state-space models
- spatiotemporal
- spiking neural networks
- brain connectivity
- SSM
Core Concepts
- Hierarchical connectome architecture
- Spatiotemporal state-space models
- Parallelized recurrent networks
- Brain-inspired connectivity patterns
- Temporal and spatial integration
Applications
- Brain connectivity modeling
- Spiking neural networks
- Temporal sequence modeling
- Neuroimaging analysis
Implementation Guidelines
When to Use This Skill
- Research involving hierarchical connectome
- Projects related to state-space models
- Analysis requiring spatiotemporal
Key Methodologies
- Data Preparation: Prepare your neural data according to the paper specifications
- Model Setup: Configure the appropriate architecture for your use case
- Training/Inference: Follow the paper's methodology for optimal results
- Evaluation: Use relevant metrics to assess performance
Tools Typically Used
- Python: NumPy, SciPy for numerical computations
- Neuroimaging: MNE, Nilearn, Brain Connectivity Toolbox
- Machine Learning: PyTorch, TensorFlow for model implementation
- Visualization: Matplotlib, Seaborn, Plotly for results
References
Source Paper
- Title: Parallelized Hierarchical Connectome: A Spatiotemporal Recurrent Framework for Spiking State-Space Models
- arXiv: 2604.01295v1
- PDF: Download
- Published: 2026-04-01
Related Skills
- Other neuroscience research skills in the collection
- Brain connectivity analysis tools
- Neural dynamics modeling frameworks
Notes
This skill was automatically generated from arXiv research as part of the neuroscience literature review workflow. For the most up-to-date information, refer to the original paper.
Last updated: 2026-04-01
