AIBullisharXiv – CS AI · Mar 37/104
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Learning Internal Biological Neuron Parameters and Complexity-Based Encoding for Improved Spiking Neural Networks Performance
Researchers developed a novel learning approach for spiking neural networks that optimizes both synaptic weights and intrinsic neuronal parameters, achieving up to 13.50 percentage point improvements in classification accuracy. The study introduces a biologically-inspired SNN-LZC classifier that achieves 99.50% accuracy with sub-millisecond inference latency.