C-SOM SNN: A Continuous Self-Organizing Map Spiking Neural Network Based on STDP Unsupervised Learning Encoding with...

To address traditional Spiking Neural Networks (SNNs) limitations in continuous variable encoding and biological plausibility, this study proposes C-SOM SNN, a novel SNN integrating SOM SNN clustering, CANN (Continuous Attractor Neural Network) continuous representation, and STDP unsupervised learning with good biological plausibility inspired by hippocampal spatial-coding neuron studies. Unlike conventional models confined to discrete variables, C-SOM SNN innovatively enables dual encoding of discrete and continuous variables via hybrid topology and Multi-Spike WTA (Winner-Take-All). Experiments validate its superiority on MNIST with fixed official train-test partition (five repeated runs with fixed seeds), and C-SOM SNN achieves 98.00 ± 0.18% accuracy versus 84.00 ± 0.40% (SOM-SNN) and 92.00 ± 0.31% (SNN-CANN). On Urban8K with official stratified 10-fold splits, it reaches 87.00 ± 0.65%, compared with 78.00 ± 0.92% (SOM-SNN) and 83.00 ± 0.84% (SNN-CANN), yielding measurable performance gains over baselines. In localization navigation, it stably encodes continuous landmark positions. It shows certain disjoint-cross-modal processing capability for visual and audio inputs in separate tasks by handling raw visual samples and audio signals transformed into image-like feature matrices. This work advances brain-inspired computing, offering an efficient, interpretable framework for complex tasks, with great potential for real-world disjoint-multi-modal applications.