SIMULTANEOUSLY RECORDED SINGLE UNITS IN THE FRONTAL-CORTEX GO THROUGHSEQUENCES OF DISCRETE AND STABLE STATES IN MONKEYS PERFORMING A DELAYED LOCALIZATION TASK

To test whether spiking activity of six to eight simultaneously record ed neurons in the frontal cortex of a monkey can be characterized by a sequence of discrete and stable states, neuronal activity is analyzed by a hidden Markov model (HMM). Using the HMM method, we are able to detect distinct states of neuronal activity within which firing rates are approximately stationary. Transitions between states, as expressed by concomitant changes in the firing rates of several units, occur qu ite abruptly, The significance and consistency of the states are confi rmed by comparison with simulated data. The detected states are specif ic to a monkey's response in a delayed localization task, allowing cor rect prediction of the response in similar to 90% of the trials. Simil ar predictive power is achieved by a model based simply on the respons e histograms (PSTH) of the units. The two models reach this predictive ability with different time courses: the PSTH model gains predictive power with a higher rate in the first second of the delay, and the HMM gains predictive power with higher rate in the next 3 sec, In this la ter period, conventional methods such as the PSTH cannot detect any fi ring rate modulations, but the HMM successfully captures transitions b etween distinct states that are specific to the monkey's behavioral re sponse and occur at highly variable times from trial to trial. Our res ults suggest that neuronal activity in this later period is described best as transitions among distinct states that may reflect discrete st eps in the monkey's mental processes.