Low-level terrain-following systems require the ability to rapidly and accurately respond to the environment to prevent inadvertent actions. Catastrophic and fatal results could occur if missed cues or latency issues in data processing are encountered. Spiking neural networks (SNNs) have the computational ability to continuously process spike trains from rapid sensory input. However, most models of SNNs do not retain information from the spike train of a previous time step because the membrane potential is rapidly reset to a resting potential after activation. A novel approach is presented, allowing the spike train of a previous time step to be 'remembered.' Results are presented showing rapid onset of a membrane potential that exceeds the threshold and spikes in the presence of the same continuous spike train without the latency of increasing the membrane potential from its resting state.