The neural mechanisms underlying memory retention and memory selection, both important ingredients of decision making, are studied via computer simulation of a biophysically realistic network model in this paper.

The investigators formulate a model of real neural assemblies in the prefrontal cortex (PFC), having a lot of similarities with neuron assemblies in real brains of humans or monkeys, and they simulate the behavior of the neuron assemblies through differential equation dynamics modeling.

They study in detail the so-called short-term depression (STD) effect of PFC neurons and also the balancing effects between inhibition and excitation inside and between different neuron assemblies. They find that STD provides essential balancing, and, in essence, controls the levels of sustained activities (measured by spikes of microcurrents and microvoltages in the presynaptic and postsynaptic areas of the neurons) of various size networks. Sustained activities are generally accepted to be the processes by which information is retained.

The results of this fine paper clarify findings of real network dynamics and point to further investigation in various directions in real networks.