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

Understand

We implement a mechanism - originally proposed as a model for the large memory storage capacity of black holes - in quantum neural networks and show that an exponentially increased capacity of pattern storage and recognition is achieved in certain critically excited states, without involvement of synaptic plasticity.

• We consider a simple network of N interconnected quantum neurons with weak excitatory synaptic connections.
• We show that for frozen synaptic weights there exist the critical states of enhanced memory storage capacity.
• These states are achieved thanks to the high excitation levels of some of the neurons, which - despite of feeble synaptic connections - dramatically lower the response threshold of the remaining weaker-excited neurons.