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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.