Supervised learning of spatial features with STDP and homeostasis using Spiking Neural Networks on SpiNNaker
The paper introduces a method to train Spiking Neural Networks (SNNs) to recognize spatial patterns—spike sequences without time components—using Spike Timing Dependent Plasticity (STDP) and homeostasis. The training process uses these mechanisms to adapt the network weights, allowing it to distinguish between different trained patterns, proving effective for tasks like image pattern recognition or network traffic analysis.