SNN Credit Assignment Problem is NOT Unsolved Anymore

Research
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The credit assignment problem in Spiking Neural Networks (SNNs) has been treated as unsolved for years due to reliance on BPTT and unstable training.

I’ve been working on a data-driven, event-based approach that enables effective credit assignment without full BPTT.

Early results show:

  • Stable training in deeper SNNs

  • Better temporal credit propagation

  • Lower compute overhead

This is backed by real experimental results, and I’m preparing a research paper.

I believe this problem is no longer “unsolved” we’re closer to practical SNN learning than people think.

Looking for collaborators and feedback (SNN, neuromorphic, biologically plausible learning).

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