Quantitative Analysis of Formation of Active Avoidance Behavior in the Hippocampus Coagulated and Intact White Albino Rats


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.

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Tsagareli, S. , Archvadze, N. and Tavdishvili, O. (2012) Quantitative Analysis of Formation of Active Avoidance Behavior in the Hippocampus Coagulated and Intact White Albino Rats. Journal of Behavioral and Brain Science, 2, 10-17. doi: 10.4236/jbbs.2012.21002.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] K. Hausken and J. F. Moxnes, “Behaviorist Stochastic Modeling of Instrumental Learning,” Behavioural Processes, Vol. 56, No. 2, 2001, pp. 121-129. doi:10.1016/S0376-6357(01)00192-9
[2] C. Kolodziejski, B. Porr and F. Worgotter, “Mathematical Properties of Neuronal TD-Rules and Differential Hebbian Learning: A Comparison,” Biological Cybernetics, Vol. 98, No. 3, 2008, pp. 259-272. doi:10.1007/s00422-007-0209-6
[3] G. McCollum, “Mathematics Reflecting Sensorimotor Organization,” Biological Cybernetics, Vol. 88, No. 2, 2003, pp. 108-128. doi:10.1007/s00422-002-0344-z
[4] M. P. Paulus and M. A. Geyer, “Quantitative Assessment of the Microstructure of Rat Behavior: I. f(d), the Extension of the Scaling Hypothesis,” Psychopharmacology, Vol. 113, No. 2, 2005, pp. 177-186. doi:10.1007/BF02245695
[5] P. E. Rapp, “Quantitative Characterization of Animal Behavior Following Blast Exposure,” Cognitive Neurodynamics, Vol. 1, No. 4, 2007, pp. 287-293. doi:10.1007/s11571-007-9027-8
[6] S. Ito, H. Yuasa, Z. Luo, M. Ito and D. Yanagihara, “A Mathematical Model of Adaptive Behavior in Quadruped Locomotion,” Biological Cybernetics, Vol. 78, No. 5, 1998, pp. 337-347. doi:10.1007/s004220050438
[7] M. C. Baker and D.M. Logue, “Population Differentiation in a Complex Bird Sound: A Comparison of Three Bio- acoustical Analysis Procedures,” Ethology, Vol. 109, No. 3, 2003, pp. 223-242. doi:10.1046/j.1439-0310.2003.00866.x
[8] D. Balslev, F. A. Nielsen, S. A. Frutiger, J. J. Sidtis, T. B. Christiansen, C. Svarer, S. C. Strother, D. A. Rottenberg, L. K. Hansen, O. B. Paulson and I. Law, “Cluster Analysis of Activity-Time Series in Motor Learning,” Human Brain Mapping, Vol. 15, No. 3, 2002, pp. 135-145. doi:10.1002/hbm.10015
[9] H. Cohen, J. Zohar, M. A. Matar, Z. Kaplan and A. B. Geva, “Unsupervised Fuzzy Clustering Analysis Supports Behavioral Cutoff Criteria in an Animal Model of Post- traumatic Stress Disorder,” Biological Psychiatry, Vol. 58, No. 8, 2005, pp. 640-650. doi:10.1016/j.biopsych.2005.04.002
[10] P. Edison, H. A. Archer, A. Gerhard, R. Hinz, N. Pavese, F. E. Turkheimer, A. Y. F. T. Hammers, N. Fox, A. Kennedy, M. Rossor and D. J. Brooks, “Microglia, Amyloid, and Cognition in Alzheimer’s Disease: An [11C] (R) PK11195-PET and [11C] PIB-PET Study,” Neurobiolpgy of Disease, Vol. 32, No. 3, 2008, pp. 412-419. doi:10.1016/j.nbd.2008.08.001
[11] C. Lochner, S. M. J. Hemmings, C. J. Kinnear, D. Nel, S. Seedat, J. C. Moolman-Smook and D. J. Stein, “Cluster Analysis of Obsessive-Compulsive Symptomatology: Iden- tifying Obsessive-Compulsive Disorder Subtypes,” Israel Journal of Psychiatry and Related Sciences, Vol. 45, No. 3, 2008, pp. 164-176.
[12] J. C. McDonagh, R. B. Gorman, E. E. Gilliam, T. G. Hornby, R. M. Reinking and D. G. Stuart, “Properties of Spinal Motoneurons and Interneurons in the Adult Turtle: Provisional Classification by Cluster Analysis,” The Journal of Comparative Neurology, Vol. 400, No. 4, 1998, pp. 544-570. doi:10.1002/(SICI)1096-9861(19981102)400:4<544::AID-CNE8>3.0.CO;2-A
[13] M. C. Stevens, D. A. Fein, M. Dunn, D. D. Allen, L. H. Waterhouse, C. M. D. Feinstein and I. M. D. Rapin, “Subgroups of Children with Autism by Cluster Analysis: A Longitudinal Examination,” Journal of the American Academy of Child & Adolescent Psychiatry, Vol. 39, No. 3, 2000, pp. 346-352. doi:10.1097/00004583-200003000-00017
[14] O. Tavdishvili, “Automatic Classification Algorithm for Observable Data Set,” Proceedings of the Institute of Cybernetics, Vol. 3, No. 1-2, 2004, pp. 136-141.
[15] O. Tavdishvili and T. Sulaberidze, “Segmentation Method of 3D Segments Extraction on the Scene Image,” In: J. M. Blackledge and M. J. Turner, Eds., Image Processing III: Mathematical Methods, Algorithms and Applications, Hor- wood Publishing, Chichester, 2001, pp. 82-88.
[16] O. Tavdishvili, N. Archvadze, S. Tsagareli, A. Stamateli and M. Gvajaia, “The Study of Rats’ Active Avoidance Behavior by the Cluster Analysis,” Life System Modeling and Intelligent Computing, Vol. 6330, 2010, pp. 180-188.
[17] J. Ferbinteanu and M. L. Shapiro, “Prospective and Ret- rospective Memory Coding in the Hippocampus,” Neuron, Vol. 40, No. 6, 2003, pp. 1227-1239. doi:10.1016/S0896-6273(03)00752-9
[18] K. Henke, V. Treyer, E. T. Nagy, S. Kneifel, M. Dursteler, R. M. Nitsch and A. Buckb, “Active Hippocampus during Nonconscious Memories,” Consciousness and Cognition, Vol. 12, No. 1, 2003, pp. 31-48. doi:10.1016/S1053-8100(02)00006-5
[19] J. Ji, and S. Maren, “Electrolytic Lesions of the Dorsal Hippocampus Disrupt Renewal of Conditioned Fear after Extinction,” Learning Memory, Vol. 12, No. 3, 2005, pp. 270-276. doi:10.1101/lm.91705
[20] K. Longden, “Constraining the Function of CA1 in Asso- ciative Memory Models of the Hippocampus,” Ph.D. Thesis, University of Edinburgh, Edinburgh, 2005.
[21] I. Mart?nez, G. L. Quirarte, S. Diaz-Cintra, C. Quiroz and R. A. Prado-Alcala, “Effects of Lesions of Hippocampal Fields CA1 and CA3 on Acquisition of Inhibitory Avoi- dance,” Neuropsychobiology, Vol. 46, No. 2, 2002, pp. 97-103. doi:10.1159/000065419
[22] D. Schulz, J. P. Huston, K. Jezek, H. L. Haas, A. Roth-Harer, O. Selbach and H. J. Luhmann, “Water Maze Performance, Exploratory Activity, Inhibitory Avoidance and Hippocampal Plasticity in Aged Superior and Inferior Learners,” European Journal of Neuroscience, Vol. 16, No. 11, 2002, pp. 2175-2185. doi:10.1046/j.1460-9568.2002.02282.x
[23] S. Tsagareli and N. Djgarkava, “The Machine Processing of Experimental Results of the Formation and Mainte- nance of Avoiding and Feeding Habits,” In: R. Zhordania, Ed., Biology and Contemporaneity, Tbilisi University Press, Tbilisi, 2002, pp. 166-177.
[24] G. Paxinos and C. Watson, “The Rat Brain in Stereotaxic Coordinates,” 3rd Edition, Academic Press, San Diego, 1997.

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