In a Hopfield network model, the two states of a neuron (firing or at rest) are denoted by the values . The future state of a neuron is determined by the present state of all the other neurons via the synaptic matrix, but is independent of its own present state. The overlap between the original and the time-evolved state of the network is a measure of "memory recall"; it depends upon the fractional memory load and a state-dependent neuron firing threshold . This Demonstration obtains the average time-evolution of patterns stored in a network of neurons () with a neuron-state-dependent firing threshold . The distribution of resulting overlaps between the time-evolved and original states, , shifts from a peak at one to a broad distribution at lower values of as the memory load increases, indicating the threshold memory capacity of the neural network.