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Rao, R.P.N. (2010) Decision Making under Uncertainty: A Neural Model Based on Partially Observable Markov Decision Processes. Frontiers in Computational Neuroscience, 4, 146.
http://dx.doi.org/10.3389/fncom.2010.00146

has been cited by the following article:

• TITLE: The Prognosis of Delayed Reactions in Rats Using Markov Chains Method

  AUTHORS: Sulkhan N. Tsagareli, Nino G. Archvadze, Otar Tavdishvili, Marika Gvajaia

  KEYWORDS: Delayed Reactions, Behavioral Algorithm, Markov Chain

  JOURNAL NAME: Journal of Behavioral and Brain Science, Vol.6 No.1, January 15, 2016

  ABSTRACT: The original modified method of the direct delayed reaction has been used for the evaluation of food-obtaining strategy across spatial learning tasks in T-maze alternation. The optimal behavioral algorithms for each experimental day have been identified so that the animals obtain maximum possible food amount with minimal number of mistakes. Markov chain method has been used for the prognosis of rat’s behavioral strategy during the spatial learning task. The learning and decision-making represent the probabilistic transition process where the animal choice at each step (state) depends on the learning experience from previous step (state).