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A recent experiment suggests that neural circuits may alternatively implementcontinuous or discrete attractors, depending on the training set up. Inrecurrent neural network models, continuous and discrete attractors areseparately modeled by distinct forms of synaptic prescriptions (learningrules). Here, we report a solvable network model, endowed with Hebbian synapticplasticity, which is able to learn either discrete or continuous attractors,depending on the frequency of presentation of stimuli and on the structure ofsensory coding. A continuous attractor is learned when experience matchessensory coding, i.e. when the distribution of experienced stimuli matches thedistribution of preferred stimuli of neurons. In that case, there is noprocessing of sensory information and neural activity displays maximal entropy.If experience goes beyond sensory coding, processing is initiated and thecontinuous attractor is destabilized into a set of discrete attractors.

Continuous or discrete attractors in neural circuits? A self-organized switch at maximal entropy