Change-Point Detection in Neuronal Spike Train Activity

Animals respond to changes in their environment based on the information encoded in neuronal spike activity. When exploring the neural substrates of behaviors such as prey detection or predator avoidance, one key issue is to determine how quickly and reliably the system can detect that a behaviorally relevant change has taken place. What are the neural mechanisms and computational principles that allow fast, reliable detection of changes in spike activity? Here we present an optimal statistical signal-processing algorithm for change-point detection, known as the CUSUM algorithm. We then show that the performance of a simple neuron model with leaky integrate-and-fire dynamics can approach theoretically optimal performance limits under certain conditions