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genpark-spike-timing-dependent-plasticity-stdp-skill

Spike-Timing-Dependent Plasticity (STDP) unsupervised Hebbian synaptic learning rule with asymmetric exponential long-term potentiation and depression.

Details

External ID
1362949060
Source
GITHUB
Company
—
Product
genpark-spike-timing-dependent-plasticity-stdp-skill
Website domain
github.com
Launched
Sept. 9, 2026
Cohort
—
Upvotes
8
Upvotes percentile
0.14514476044068664
Tags
agentic-ai, genpark-skill, hebbian-learning, spiking-neural-networks, stdp, synaptic-plasticity
Fetched at
Sept. 13, 2026, 5:48 a.m.
Updated at
Sept. 13, 2026, 5:48 a.m.

Enrichment

Theme
specialized AI models and agent reasoning tools
Vertical
Horizontal
Function
Dev tools
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
stdp hebbian synaptic learning rule implementation for neural networks
Manually corrected
False

Could you build this?

Yes Spike-Timing-Dependent Plasticity (STDP) with exponential weight modification based on spike arrival times is mathematically simple and easily implemented in Python or NumPy as a standalone neural network training rule.

Competitors

Other products that read as similar to this one — 56 launches clear the similarity bar, closest 8 shown.

Attention rank: #15 of 57 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 247 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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