Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a ${(ln2)}^{-N}$-fold increase in capacity for an $N$-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices.

• Received 27 November 2017

DOI:https://doi.org/10.1103/PhysRevE.97.030301