  The Self-Organizing Map (SOM) is a brain-inspired neural model that is very
promising for unsupervised learning, especially in embedded applications.
However, it is unable to learn efficient prototypes when dealing with complex
datasets. We propose in this work to improve the SOM performance by using
extracted features instead of raw data. We conduct a comparative study on the
SOM classification accuracy with unsupervised feature extraction using two
different approaches: a machine learning approach with Sparse Convolutional
Auto-Encoders using gradient-based learning, and a neuroscience approach with
Spiking Neural Networks using Spike Timing Dependant Plasticity learning. The
SOM is trained on the extracted features, then very few labeled samples are
used to label the neurons with their corresponding class. We investigate the
impact of the feature maps, the SOM size and the labeled subset size on the
classification accuracy using the different feature extraction methods. We
improve the SOM classification by +6.09\% and reach state-of-the-art
performance on unsupervised image classification.
