  Backpropagation-based supervised learning has achieved great success in
computer vision tasks. However, its biological plausibility is always
controversial. Recently, the bio-inspired Hebbian learning rule (HLR) has
received extensive attention. Self-Organizing Map (SOM) uses the competitive
HLR to establish connections between neurons, obtaining visual features in an
unsupervised way. Although the representation of SOM neurons shows some
brain-like characteristics, it is still quite different from the neuron
representation in the human visual cortex. This paper proposes an improved SOM
with multi-winner, multi-code, and local receptive field, named mlSOM. We
observe that the neuron representation of mlSOM is similar to the human visual
cortex. Furthermore, mlSOM shows a sparse distributed representation of
objects, which has also been found in the human inferior temporal area. In
addition, experiments show that mlSOM achieves better classification accuracy
than the original SOM and other state-of-the-art HLR-based methods. The code is
accessible at https://github.com/JiaHongZ/mlSOM.
