The MI quantifies how much information one variable provides about the other. In this case, if a neuron tends to fire much more frequently on drinking trials than non-drinking trials, for instance, a large MI value would result. However, if drinking status and neuron firing rate were unrelated, then a small MI value would result. By calculating the MI at each time bin for each neuron, we were able to evaluate encoding dynamically throughout the task.
