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Neuromorphic
Research papers, repositories, and articles about neuromorphic
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Gradient-Free Training of Spiking Neural Networks via Low-Rank Evolution Strategies
The authors train spiking neural networks without backprop, using a low-rank evolution strategy that scales to larger models. They match or beat surrogate-gradient baselines on benchmarks. If you care about neuromorphic hardware or energy-efficient AI, this is a cleaner path than shoehorning backprop into spikes.
Dhruv Patankar, Sachit Ramesha Gowda
Neuro-Symbolic Activation Discovery: Transferring Mathematical Structures from Physics to Ecology for Parameter-Efficient Neural Networks
Using genetic programming, the author mines custom activation functions from physics data and reuses them in ecology models. These bespoke activations match accuracy with far fewer parameters.
Anas Hajbi