One-Shot Object Appearance Learning using Spiking Neural Networks
This project is a Spiking Neural Network used for testing one-shot object appearance learning. That is the learning of new object features based on one or very few training instances.
It is written in Python and runs on the NEST neurosimulator, which gives the framework a better biological plausibility over other networks of this kind, which use own implementations for the neural mechanics.