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Journal of Neuroscience, Vol 16, 752-768, Copyright © 1996 by Society for Neuroscience
Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task
E Seidemann, I Meilijson, M Abeles, H Bergman and E Vaadia
School of Mathematical Sciences, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Ramat Aviv, Israel.
To test whether spiking activity of six to eight simultaneously recorded
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 quite
abruptly. The significance and consistency of the states are confirmed by
comparison with simulated data. The detected states are specific to a
monkey's response in a delayed localization task, allowing correct
prediction of the response in approximately 90% of the trials. Similar
predictive power is achieved by a model based simply on the response
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 later period,
conventional methods such as the PSTH cannot detect any firing rate
modulations, but the HMM successfully captures transitions between distinct
states that are specific to the monkey's behavioral response and occur at
highly variable times from trial to trial. Our results suggest that
neuronal activity in this later period is described best as transitions
among distinct states that may reflect discrete steps in the monkey's
mental processes.
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