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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.
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基于 SOC 职业分类
| 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"}} |
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).
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
