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lif-neuron

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Neural Preference Learning (NPL) is a novel architecture that gives LLM agents persistent, personal preferences by pairing them with a companion spiking neural network. Unlike RLHF (which is batch, pre-deployment, and population-level), NPL operates in real-time, learning from individual user feedback through natural language.

  • Updated Mar 16, 2026
  • JavaScript

Hardware-based 3-layer SNN prototype (3-2-1 architecture) using discrete 74LS series logic. Features event-driven temporal pattern recognition via 74LS161 integration and 74LS85 thresholding. Validated at 10kHz with asynchronous RC reset loops to mimic biological refractory periods. Developed as a standalone hardware neural inference engine.

  • Updated Aug 19, 2026

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