neural-connectivity-bayesnet
importedsoftware/neural-connectivity-bayesnet
In this project, I investigate the possibility of modelling neural connectivity from functional magnetic resonance images using Bayesian Networks.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Documentation
- unknown
- Tags
- bayesian-networks · neuroimaging · neuroscience
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- causal_twitter_modeling_covid19bayesian-networks
Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.
- APPIANneuroimaging · neuroscience
APPIAN is an open-source automated software pipeline for analyzing PET images in conjunction with MRI. The goal of APPIAN is to make PET tracer kinetic data analysis easy for users with moderate…
- brainextractorneuroimaging · neuroscience
Brain Extraction Tool in Python
- brainiakneuroimaging · neuroscience
Brain Imaging Analysis Kit
- brainspell-neoneuroimaging · neuroscience
Working on a new version of Brainspell (an open-source platform for neuroimaging literature) to make a public JSON API that collaborators can contribute to, switch out the stack for better…
- confusiusneuroimaging · neuroscience
Python package for analysis and visualization of functional ultrasound imaging data.
- api.github.com/repos/nihil21/neural-connectivity-bayesnetretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2021-07-20, 4 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/38.json→ .entries["neural-connectivity-bayesnet"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.