In computer science, 'convolutional neural networks' are used, that are meant to be inspired by biological network structures like found in the human brain visual cortex.
In the computer implementation, the 'convolution' means that any neuron of a certain 'layer' is linked with several spatial adjacent neurons in the previous layer. When those networks are trained, the 'weights', which are analog to synaptic strength, of all neurons of one layer have an equivalent local topology, and thus can be mapped between the neurons, and finally unified. This way, only one 'weight' is trained for all equivalent synapses on all neurons of an layer.
Is there any biological mechanism found that may sport this feature? My guess would be that the vision cortex is partly organised by self structuring not depending on neural activation to achieve low level vision processing. Otherwise, all neurons in one 'layer' would need a way to exchange information to synchronize their synaptic structure.
However, I guess that the human brain would have some mechanism of this kind, as it would provide for example the ability to learn the detection of certain visual patterns or events independent of their retinal position, without learning the detection individually for every image location.
Is any such mechanism known or how could it work?