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Quantitative Analysis of Formation of Active Avoidance Behavior in the Hippocampus Coagulated and Intact White Albino Rats
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作者 Sulkhan N. Tsagareli Nino G. Archvadze Otar Tavdishvili 《Journal of Behavioral and Brain Science》 2012年第1期10-17,共8页
Unsupervised cluster analysis is proposed for analysis of active avoidance formation in three groups of albino rats: 1) intact;2) with electrolytic lesions of neocortex over the dorsal hippocampus;and 3) with electrol... Unsupervised cluster analysis is proposed for analysis of active avoidance formation in three groups of albino rats: 1) intact;2) with electrolytic lesions of neocortex over the dorsal hippocampus;and 3) with electrolytic lesions of dorsal hippocampus. The term “behavior vector” has been introduced to assess quantitatively the behavior of rats while learning. The proposed approach enables to assess active avoidance behavior in rats simultaneously by all the test parameters: 1) reaction to the light;2) reaction to the electric irritation;and 3) inter-trial spontaneous behavior. The animals were grouped by their behavioral resemblance through the learning process. The proposed method facilitates the assessment of learning capacities in animals and paves way for getting additional information concerning correlative relationships between their learning skills and other neuroethological and neurobiological parameters. 展开更多
关键词 BEHAVIOR Learning Memory Active AVOIDANCE HIPPOCAMPUS NEOCORTEX UNSUPERVISED Clustering
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The Prognosis of Delayed Reactions in Rats Using Markov Chains Method
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作者 Sulkhan N. Tsagareli Nino G. Archvadze +1 位作者 Otar Tavdishvili Marika Gvajaia 《Journal of Behavioral and Brain Science》 2016年第1期19-27,共9页
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 e... 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). 展开更多
关键词 Delayed Reactions Behavioral Algorithm Markov Chain
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