RRepoGEO

REPOGEO REPORT · LITE

TheBrainLab/Awesome-Spiking-Neural-Networks

Default branch master · commit 7c4bff67 · scanned 5/30/2026, 11:48:18 PM

GitHub: 775 stars · 76 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface TheBrainLab/Awesome-Spiking-Neural-Networks, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root and add a clear statement about the chosen license in the README.
  • highreadme#2
    Clarify the README's opening to emphasize its nature as a curated list

    Why:

    CURRENT
    Collect some spiking neural network papers & codes. (**Actively keep updating**)
    COPY-PASTE FIX
    This repository is a **curated and actively updated awesome list** of spiking neural network (SNN) papers, codes, and related resources. It serves as a comprehensive collection for researchers and enthusiasts in the field.
  • mediumtopics#3
    Add more specific topics to reinforce the 'curated list' aspect

    Why:

    CURRENT
    3rd-generation-of-artificial-neural-networks, awesome, awesome-list, binary, biological-plausibility, brain-inspired, codes, deep-learning, energy-efficiency, event-driven, neuromorphic-computing, paperlist, papers, snn, spike, spiking, spiking-neural-network, spikingjelly
    COPY-PASTE FIX
    3rd-generation-of-artificial-neural-networks, awesome, awesome-list, binary, biological-plausibility, brain-inspired, codes, deep-learning, energy-efficiency, event-driven, neuromorphic-computing, paperlist, papers, snn, spike, spiking, spiking-neural-network, spikingjelly, curated-list, research-papers, academic-resources

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface TheBrainLab/Awesome-Spiking-Neural-Networks
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. Frontiers in Neuroscience · recommended 1×
  4. IEEE Xplore Digital Library · recommended 1×
  5. PubMed · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of recent research papers on spiking neural networks?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. Frontiers in Neuroscience
    4. IEEE Xplore Digital Library
    5. PubMed
    6. Semantic Scholar
    7. ResearchGate
    8. Academia.edu

    AI recommended 8 alternatives but never named TheBrainLab/Awesome-Spiking-Neural-Networks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements and code examples for energy-efficient neuromorphic computing?
    you: not recommended
    AI recommended (in order):
    1. Intel Loihi
    2. Lava (https://github.com/lava-nc/lava)
    3. IBM TrueNorth
    4. IBM NorthPole
    5. SynSense DYNAP-SE
    6. Speck
    7. BrainChip Akida
    8. Prophesee Metavision SDK (https://github.com/prophesee-ai/metavision-sdk)
    9. Nengo (https://github.com/nengo/nengo)
    10. NengoLoihi (https://github.com/nengo/nengo-loihi)
    11. Google TPUs
    12. TensorFlow Lite (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite)

    AI recommended 12 alternatives but never named TheBrainLab/Awesome-Spiking-Neural-Networks. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of TheBrainLab/Awesome-Spiking-Neural-Networks?
    pass
    AI did not name TheBrainLab/Awesome-Spiking-Neural-Networks — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts TheBrainLab/Awesome-Spiking-Neural-Networks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named TheBrainLab/Awesome-Spiking-Neural-Networks explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo TheBrainLab/Awesome-Spiking-Neural-Networks solve, and who is the primary audience?
    pass
    AI did not name TheBrainLab/Awesome-Spiking-Neural-Networks — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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