Neural ensemble states in the prefrontal cortex identified using a hidden Markov model with a modified EM algorithm

A modification to the standard estimation–maximization algorithm is presented, that allows the identification of neural ensemble states without predetermination of the number of states. Since the actual number of states a neural ensemble goes through in a given time period may vary from trial to trial, this represents a better description than using a predetermined, fixed number of states. The algorithm is used to identify ensemble states of simultaneously recorded neurons in the prefrontal cortex of behaving monkeys while they were freely viewing scenes containing multiple objects. We demonstrate that state transitions are correlated with behavioral and trial events