<p>Theoretical studies on memory capacity in artificial neural networks have shown the number of storable memories scale with the number of neurons and synapses in the network. As the memory capacity limit is reached, then stored memories interfere, and recall performance is reduced. A well-established neuromorphic microcircuit model was employed to systematically evaluate its recall performance as a function of stored patterns, interference, contextual information, network size, and engram cells when specific synaptic connections in the network were strengthened. The model consisted of multi-compartmental Hodgkin-Huxley-based excitatory (pyramidal) cells and two types of inhibitory neurons (bistratified cell, oriens lacunosum-moleculare (OLM) cell) firing at specific phases of a theta oscillation imposed by an external inhibitory signal targeting only the inhibitory cells in the network. Inhibitory cells inhibited specific compartments of the network’s excitatory cells. Two excitatory inputs (sensory and contextual inputs) targeted dendritic compartments of cells in the network and caused cells to fire. Simulation results showed that out of six model variants tested strengthening of excitatory synapses in proximal but not basal dendrites of bistratified cells inhibiting pyramidal cells (model 1) made recall perfect. Strengthening of inhibitory synapses in pyramidal cells (model 2) made recall worst. Decreasing the number of engram cells coding for a memory pattern improved recall in a pathway-dependent way. However, increases in network size had a small effect on improving memory recall and so did increases in stored patterns. Interference between stored patterns had a detrimental effect on recall, which was reversible as the number of engram cells decreased. Changes in contextual information made recall worse confirming previous evidence that more familiar context facilitates memory retrieval.</p>