NotEnoughSnow/lorenz-spiking-reservoir

This is a personal research exploration at a very early stage (25 days old, 0 stars, 0 forks, no velocity). Spiking neural networks, reservoir computing, and bio-plausible learning rules are well-established research areas with existing implementations (Brian2, Nengo, Norse, SpiNNaker software stacks). The project appears to be a from-scratch implementation for learning purposes rather than a novel algorithmic or architectural contribution. The neuromorphic hardware focus is niche and valuable as a research artifact, but lacks the adoption, ecosystem integration, or technical novelty needed for defensibility. Frontier labs (OpenAI, Anthropic, Google) have minimal incentive to compete here—this sits in academic/research territory where specialized frameworks and established neuromorphic platforms (Intel, Heidelberg) already dominate. The low barrier to entry for similar projects, combined with zero community traction, places this firmly in tutorial/learning-project territory. Defensibility improves only if the author demonstrates a novel learning rule variant, a novel hardware mapping, or significant performance advantages over existing frameworks—none evident from the description.