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We implement a mechanism - originally proposed as a model for the largememory storage capacity of black holes - in quantum neural networks and showthat an exponentially increased capacity of pattern storage and recognition isachieved in certain critically excited states, without involvement of synapticplasticity. We consider a simple network of N interconnected quantum neuronswith weak excitatory synaptic connections. We show that for frozen synapticweights there exist the critical states of enhanced memory storage capacity.These states are achieved thanks to the high excitation levels of some of theneurons, which - despite of feeble synaptic connections - dramatically lowerthe response threshold of the remaining weaker-excited neurons. As a results,the latter neurons acquire a capacity to store an exponentially large number ofpatterns within a narrow energy gap. The stored patterns can be recognized andretrieved with perfect response under the influence of arbitrarily soft inputstimuli. In sharp contrast, under the same stimuli the recall is absent in theground-state of the system. The lesson is that the state with the highestmicro-state entropy and memory storage capacity is not necessarily a localminimum of energy, but rather an excited critical state. The consideredphenomenon has a smooth classical limit and can serve for achieving an enhancedmemory storage capacity in classical brain networks.

Critically excited states with enhanced memory and pattern recognition capacities in quantum brain networks: Lesson from black holes