Announcing Open Neuromorphic Research: Community Peer Review for Open Science

Submit your open-source neuromorphic projects for transparent community review and recognition through the ONR Program.

Announcing Open Neuromorphic Research: Community Peer Review for Open Science
Photo by Manavya

Call for Papers: Open Neuromorphic Research

Open Neuromorphic is launching Open Neuromorphic Research a peer review initiative designed to highlight open, reproducible, and high-impact work in neuromorphic computing. Submissions are reviewed by volunteers from the community and, if approved, receive the ONM Community Approved badge along with a certificate page in our Approved Research Registry.

Whether you’re submitting a research paper, codebase, dataset, or educational tool, ONR gives you a pathway to visibility, constructive feedback, and community validation.

Why Participate?

  • Get Recognized: Approved projects receive a badge and are showcased in our registry
  • Get Reviewed: Receive constructive feedback from expert volunteers
  • Build Trust: Show your commitment to open, transparent science
  • Accelerate the Field: Help build a shared ecosystem of reproducible tools

What Can You Submit?

We accept open-source neuromorphic projects including:

  • Research codebases (e.g. SNN models, simulators)
  • Publication-style papers
  • Datasets and preprocessing pipelines
  • Educational resources, tutorials, and docs
  • Hardware libraries (e.g. for Loihi, Speck, etc.)
  • Analysis or visualization tools

Submissions may be GitHub repositories, Jupyter notebooks, whitepapers, or IEEE-formatted papers. All submissions must meet our Definition of Open.

How It Works

  1. Prepare Your Submission: Follow the Submitter’s Guide
  2. Submit via OpenReview: Use the ONR Submission Portal
  3. Community Review: Your submission is reviewed by 3–5 volunteers, based on criteria like clarity, reproducibility, and contribution
  4. Receive Recognition: Approved projects are awarded a badge and added to our public registry

Review Criteria

Reviews focus on:

  • Relevance to neuromorphic computing
  • Clarity and documentation
  • Reproducibility of code/methods
  • Technical rigor
  • Openness (permissive license, transparent methods)
  • Community value (benefit to students, researchers, etc.)

Details available in the Review Criteria.

Timeline

  • Submissions: Accepted on a rolling basis
  • Review turnaround: Typically within 1 month (depending on submission size)
  • Decisions: Constructive feedback provided for all submissions. Submissions are either accepted or revisions are requested.

Get Involved

About ONR

The ONR initiative supports transparent, community-led peer review in neuromorphic computing. Our mission is to elevate high-quality, open-source projects and promote practices that make research more reproducible, collaborative, and impactful.

For all details, visit the ONR Hub.

About the Authors

Jens E. Pedersen

Jens E. Pedersen

Doctoral student at KTH, modeling neuromorphic systems to solve real-world challenges. Maintainer of Norse, AEStream, Faery, and co-author of NIR.
Justin Riddiough

Justin Riddiough

Strategic digital solutions partner and open-source advocate. As Vice-Chair of the ONM Executive Committee, Justin focuses on building robust digital infrastructure and fostering community growth.
Danny Rosen

Danny Rosen

Danny Rosen is a Master’s student in Computer Engineering at Virginia Tech’s Innovation Campus in Alexandria, Virginia. He’s currently researching Spiking Neural Networks (SNNs) for edge-based signal processing.

Have an idea? Share your voice.

Open Neuromorphic is a community-driven platform. We invite you to share your research, tutorials, or insights by writing a blog post.

Related Posts

Strategic Vision for Open Neuromorphic

Strategic Vision for Open Neuromorphic

Why 'open' matters and where we want to take the Open Neuromorphic community

NorthPole, IBM's latest Neuromorphic AI Hardware

NorthPole, IBM's latest Neuromorphic AI Hardware

A deep dive into IBM's NorthPole, a brain-inspired AI accelerator. Understand its architecture, 10 core axioms, and how it achieves groundbreaking energy efficiency for neural inference.

Spiking Neural Network (SNN) Library Benchmarks

Spiking Neural Network (SNN) Library Benchmarks

Discover the fastest Spiking Neural Network (SNN) frameworks for deep learning-based optimization. Performance, flexibility, and more analyzed in-depth