The human brain is comprised of many billions of neurons, which then connect with each other many trillions of times. Modeling how these neurons function provides insight into language, thought, and behavior. However, neurons are not all identical, so an approach that performs well on some neurons will perform poorly on others. I focused on a learning system to detect and model neurons that use pattern-recognition in order to decide when to fire, which would not be expressed well under existing approaches. We researched a strategy for describing these process in a non-Markov state, which means that we do not have enough information to correctly model the neuron, by approximating to Markov models using a subset of the required information. This means that we produce a set of probabilities for the next state of the neuron given the previous state. We approximate the Markov model using Monte-Carlo Tree Search (MCTS) optimizing for smallest confidence interval to select sequences to measure. In order to calculate a confidence interval on a given sequence we apply A*, pronounced A star, which is a targeted pathfinding algorithm, in order to produce execution paths that in turn find confidence intervals. We use two strategies for pruning sequences; a MCTS to find the measured sequence that differs least from chance for long term learning, and exponential decay for short term learning. We assume that after we process information it cannot be recovered, so when we measure new sequences we only measure from the time of consideration onward. If our process successfully locates important patterns the neuron is looking for, it produces an accurate approximation for the neuron that can be rapidly evaluated.