hierarchical-connectome-ssm

---
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
1. **Data Preparation**: Prepare your neural data according to the paper specifications
2. **Model Setup**: Configure the appropriate architecture for your use case
3. **Training/Inference**: Follow the paper's methodology for optimal results
4. **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](https://arxiv.org/abs/2604.01295v1)
- **PDF**: [Download](https://arxiv.org/pdf/2604.01295v1)
- **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_