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SOM-based algorithms have been used to model several features of natural visual processing.⇒

Miikulainen et al. use their SOM-based algorithms to model the visual cortex.⇒

Miikulainen et al. use a hierarchical version of their SOM-based algorithm to model natural development of visual capabilities.⇒

A SOM that is to learn continuously cannot continuously decrease neighborhood interaction width and learning rate.

It is helpful if these parameters are self-regulated, like in PSOM.⇒

Adams et al. present a Spiking Neural Network implementation of a SOM which uses

- spike-time dependent plasticity
- a method to adapt the learning rate
- constant neighborhood interaction width⇒

Adams et al. note that there have been a number of attempts at spiking SOM implementations (and list a few).⇒

If the noise in the inputs to my SOM isn't uncorrelated between input neurons, then the SOM cannot properly learn a latent variable model.⇒

There can be situations where my algorithm is still optimal or near-optimal.⇒

There have been many extensions of the original SOM ANN, like

- (Growing) Neural Gas
- adaptive subspace SOM (ASSOM)
- Parameterized SOM (PSOM)
- Stochastic SOM
- recursive and recurrent SOMs⇒

Recursive and Recurrent SOMs have been used for mapping temporal data.⇒