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

Rainer, G; Miller, EK

doi:10.1016/S0925-2312(00)00266-6

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

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

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://www.sciencedirect.com/science/article/pii/S0925231200002666
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Rainer, G., & Miller, E. (2000). Neural ensemble states in the prefrontal cortex identified using a hidden Markov model with a modified EM algorithm. Neurocomputing, 32-33, 961-966. doi:10.1016/S0925-2312(00)00266-6.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E5B1-B

Abstract

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.