A plastic multilayer network of the early visual system inspired by the neocortical circuit

The ability of the human visual system for object recognition is remarkable. A better understanding of its processing would lead to better computer vision systems and could improve our understanding of the underlying principles which produce intelligence.
We propose a computational model of the visual areas V1 and V2, implementing a rich connectivity inspired by the neocortical circuit. We combined the three most important cortical plasticity mechanisms: 1) Hebbian synaptic plasticity to learn the synapse strengths of excitatory and inhibitory neurons, including trace learning to learn invariant representations. 2) Intrinsic plasticity to regulate the neurons responses and stabilize the learning in deeper layers. 3) Structural plasticity to modify the connections and to overcome the bias for the learnings from the initial definitions.
Among others, we show that our model neurons learn comparable receptive fields to cortical ones. We verify the invariant object recognition performance of the model. We further show that the developed weight strengths and connection probabilities are related to the response correlations of the neurons. We link the connection probabilities of the inhibitory connections to the underlying plasticity mechanism and explain why inhibitory connections appear unspecific.
The proposed model is more detailed than previous approaches. It can reproduce neuroscientific findings and fulfills the purpose of the visual system, invariant object recognition.