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Ravulakollu et al. loosely use the super colliculus as a metaphor for their robotic visual-auditory localization.

Adams et al. use SOM-like algorithms to model biological sensori-motor control and develop robotic sensori-motor controllers.

Adams et al. state that others have used SOM-like algorithms for modelling biology and for robotic applications, before (and list examples).

Ijspert et al. show in an actual robot how the same spinal central pattern generators can produce swimming and walking behavior in a robotic model of a salamander.

Ijspert et al. use their robotic model of a salamander to test hypotheses about the neural networks that produce swimming and walking behaviors in salamanders.

When he introduced his model of a computing machine, Alan Turing designed it to mimic human computation.

A system that stores multiple trained speech recognition models for different environments and retrieves them guided by visual scene recognition has improved speech recognition in reverberated and noisy environments.

The concept of reduction of the dimensionality of motor space by using motor synergies has been used in robotics.

Kohonen implies that neighborhood interaction in SOMs is an abstraction of chemical interactions between neurons in natural brain maps, which affect those neurons' plasticity, but not their current response.

Kohonen implies that neighborhood interaction in SOMs is what separates them from earlier, more bio-inspired attempts at input-driven self-organization, and what leads to computational tractability on the one hand and proper self-organization as found in natural brain maps on the other.

Kohonen states that early SOMs were meant to model brain maps and how they come to be.

So, I've returned to the roots and found something interesting for applications!