Regular Hebbian learning leads to all neurons responding to the same input. One method to force neurons to specialize is competitive learning.⇒
Competitive learning can be implemented in ANN by strong, constant inhibitory connections between competing neurons.⇒
Simple competitive neural learning with constant inhibitory connections between competing neurons leads to grandmother-type cells.⇒
A network with Hebbian and anti-Hebbian learning can produce a sparse code. Excitatory connections from input to output are learned Hebbian while inhibition between output neurons are learned anti-Hebbian.⇒
Mixing Hebbian (unsupervised) learning with feedback can guide the unsupervised learning process in learning interesting, or task-relevant things.⇒
The model due to Weber and Triesch combines SOM- or K-Means-like learning of features with prediction error feedback as in reinforcement learning. The model is thus able to learn relevant and disregard irrelevant features.⇒
Pavlou and Casey model the SC.
They use Hebbian, competitive learning to learn and topographic mapping between modalities.
They also simulate cortical input.⇒
The model due to Cuppini et al. develops low-level multisensory integration (spatial principle) such that integration happens only with higher-level input.
In their model, Hebbian learning leads to sharpening of receptive fields, overlap of receptive fields, and Integration through higher-cognitive input.⇒
Pitti et al. use a Hebbian learning algorithm to learn somato-visual register.⇒