On Comparative Study for Two Selective Ant Colony's Optimal Decisions Versus Reconstruction Problem Solving by a Mouse Inside Figure of Eight (8) Maze
This piece of research addresses an interdisciplinary comparative study of two environmental challenging phenomenal issues. Both were associated to two nonhuman creatures characterized by their behavioral intelligent performance concerned with optimal diverse decisions paradigms. More specifically, this paper deals with the comparison study for analogical behavioral learning of social insects (Ants) colony performance, versus performed behavioral learning achievement by an animal considering a mouse inside Figure of eight (8) maze via its brain hippocampus "time cells" neurons.
In more precise details, this paper firstly have demonstrated for Ant Colony System (ACS) the two effective optimal selectivity decisions for : a) The best source location between two food sources that are equidistantly sited away from the original home nest, based upon pheromone trails and following the tandem running regulation & b) The balanced selection performance with the migration speed, in order to minimize exposure to a hostile environment to avoid vulnerability to presumable danger. Secondly, the optimal decisional issue is demonstrated related to mouse's behavioral learning intelligent approach which observed in practice following its active sequential trials aiming to reach the optimal solution for a reconstruction problem during its movement inside figure of eight (8) maze. Finally, after running of realistic Artificial Neural Networks' (ANNS) simulation programs. Interestingly, the obtained results considering models of both of suggested intelligent behavioral learning issues characterized by relevant functional analogy considering changed number of artificial ant agents versus the various number of neurons inside mouse's hippocampus brain area.
(1) Ted R. Schultz " In search of ant ancestors Proc Natl Acad Sci U S A. 2000 Dec 19; 97(26): 14028–14029. Published online 2000 Dec 5 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC34089/ doi: 10.1073/pnas.011513798
(2) "Ants: Fun Facts about Ants & Ant Information for Kids - Pest World for Kids". Available online at: https://pestworldforkids.org/pest-guide/ants/
(3) E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press US,999.
(4) M. Dorigo and T. Stutzle. Ant Colony Optimization. MIT Press,2004.
(5) J. Kennedy, R. C. Eberhart, and Y. Shi. Swarm Intelligence. Morgan Kaufmann, 2001.
(6) How ants communicate? Available on line at http://www.youtube.com/watch?v=gcHt5n3NGK0 Uploaded on Jul 28, 2011.
(7) Seid, M. A.; Castillo, A.; Wcislo, W. T. (2011). "The Allometry of Brain Miniaturization in Ants". Brain, Behavior and Evolution. 77 (1): 5–13. Available online at: https://www.karger.com/Article/Abstract/322530.
(8) John, and Sarah "Interesting Facts About Ants" Published at Free Materials (C) 1996. Available online at: http://www.lingolex.com/jstefl.htm
(9) Bjorn Carey "Ants help each other as teachers and pupils" Available Online by date Jan 11, 2006 at: http://www.nbcnews.com/id/10806078/ns/technology_and_science-science/t/ants-help-each-other-teachers-pupils/#.Ws5hLS5ubIU , © 2012 Live Science.com. All rights reserved.
(10) Ivan D. Chase, Abhijit V. Deshmukh & Naga Krothapalli:" How do Ants Decide Between Food Sources of Different Values? An evaluation of the Current Explanation and Associated Mathematical Models" Published
at the PROCEEDINGS of the 2nd International Workshop on the Mathematics and Algorithms of Social Insects Georgia Institute of Technology, Atlanta, GA.30332, December 15–17, 2003, pp. 41-46.
(11) O’Shea-Wheller, T. A. et al. (2016). Migration control: a distance compensation strategy in ants, The Science of Nature, DOI 10.1007/s00114-016-1386.
(12) Hassan M. H. Mustafa, Fadhel Ben Tourkia, Ramadan Mohamed Ramadan"On Analysis and Evaluation of Comparative Performance for Selected Behavioral Neural Learning Models versus One Bio-Inspired Non-Neural Clever Model (Neural Networks Approach) Open Access Library Journal, Vol.3 No.10, October 31, 2016.
(13) Hassan M. H. Mustafa, and Fadhel Ben Tourkia "On Comparative Analysis and Evaluation Of Social Insect Colonies' Behavior During Exploring Food Sources and Their Migration to A New Nest Versus Two of Neural Networks' Learning Paradigms. (Tandem Running Approach)" Published Journal IJATTMAS volume III issue XI. Nov. 2017 Page 33-41.
(14) Hassan M. H. Mustafa, Fadhel Ben Tourkia. "On Application of Neural Networks' Modeling for Analytical Comparative Study between Two Optimally Selected Made Decisions by Ant Colony Systems". American Journal of Educational Research. 2018; 6(4):308-318. doi: 10.12691/education-6-4-3.
(15) Zhang , K. , Genzburg ,I. ,and Sejnowski,T.J. , 1998 "Interpreting neuronal population activity by reconstruction” Journal of Neurophysiology, 79:1017- 44,1998.
(16) Zhang, Iris Ginzburg, Bruce L. Mcnaughton, and Terrence J. Sejnowski "Interpreting Neuronal Population Activity by Reconstruction: Unified Framework With Application to Hippocampal Place Cells". Downloaded from http://jn.physiology.org/ by 10.220.33.3 on October 29, 2016.
(17) Singer et al. (2013) "Hippocampus: Remembering the Choices" published at Neuron. Mar 20, 2013; 77(6): 999–1001.Available 0nline-at: http://www.researchgate.net/publication/236073863_Hippocampus_remembering_the_choices
(18) Kraus BJ1, Robinson RJ 2nd, White JA, Eichenbaum H, Hasselmo ME "Hippocampal "time cells": time versus path integration" .Neuron. 2013 Jun 19;78(6):1090-101. doi: 10.1016/j.neuron.2013.04.015. E pub 2013 May 23. Available online at http://www.ncbi.nlm.nih.gov/pubmed/23707613
(19) Howard Eichenbaum "Hippocampus: Remembering the Choices" Neuron, Volume 77, Issue 6, p999–1001, 20 March 2013.
(20) Sejnowski,T.J ,1999:Neural pulse coding” foreword article for (Pulsed neural networks), MIT press, 1999, pp 13-23.
(21) Wilson, M. A. and McNaughton, B. L., Dynamics of the hippocampal ensemble code for space. Science. 1993 Aug 20; 261(5124):1055-8. Available online at http://www.ncbi.nlm.nih.gov/pubmed/8351520
(22) Fukaya, M., et al. Two level Neural Networks: Learning by Interaction with Environment, 1st ICNN, San Diego, 1988.
(23) Yunlong Liu and Hiroki Yokota " Artificial ants deposit pheromone to search for regulatory DNA elements".Available online at: https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-7-221.Published: 30 August 2006.The-image-available-online-at:
(24) S. Goss, R. Beckers,J. L. Deneubourg, S. Aron, J. M. Pasteels " How Trail Laying and Trail Following can Solve Foraging Problems For Ant Colonies" Behavioral Mechanisms of Food Selection pp 661-678. NATO AS! Series, Vol. G 20 Behavioural Mechanisms of Food Selection Edited by R. N. Hughes © Springer-Verlag Berlin Heidelberg 1990. Available online at: https://link.springer.com/content/pdf/10.1007/978-3-642-75118-9_32.pdf
(25) Wilson EO (1971). The insect societies. Harvard University Press, Cambridge Massachussets
(26) Simon Garnier, Maud Combe, Christian Jost, and Guy Theraulaz "Do
Ants Need to Estimate the Geometrical Properties of Trail Bifurcations to Find an Efficient Route? A Swarm Robotics Test Bed". Published: on March 28, 2013.
(27) Deneubourg JL, Aron S, Goss S, Pasteeis JM. (1989a) The self-organizing exploratory pattern of the Argentine ant. J Ins Behav in press.
(28) Sasaki, Bert Hölldobler, Jocelyn G. Millar, Stephen C. Pratt "A context-dependent alarm signal in the ant Temnothorax rugatulus" . .Pubished at the Journal of Experimental Biology 2014 217: 3229-3236; doi: 10.1242/jeb.106849.-Available-online-at: http://jeb.biologists.org/content/217/18/3229 .
(29) Blum, M. S. (1969). Alarm pheromones. Annu. Rev. Entomol. 14, 57-80.
(30) Blum, M. S. (1985). Alarm pheromones. In Comprehensive Insect Physiology, Biochemistry and Pharmacology: Behaviour, Vol. 9 (ed. G. A. Kerkut and L. I. Gilbert), pp. 193-224. New York, NY: Pergamon Press.
(31) Crewe, R. M. and Fletcher, D. (1974). Ponerine ant secretions: the mandibular gland secretion of Paltothyreus tarsatus Fabr. J. Entomol. Soc. South Africa 37, 291-298.
(32) Pratt S.C. (2010) Nest Site Choice in Social Insects. In: Breed M.D. and Moore J., (eds.) Encyclopedia of Animal Behavior, volume 2, pp. 534-540 Oxford: Academic Press. Available online at: http://www.elsevier.com/locate/permissionusematerial
(33) Pavlov, I.P. Conditional Reflex, An Investigation of The Psychological Activity of the Cerebral Cortex, New York, Oxford University press, 1927.
(34) Hampson, S.E. Connectionistic Problem Solving, Computational Aspects of Biological Learning, Berlin, Birkhouser, 1990.
(35) Thorndike E.L. Animal Intelligence, Darien, Ct. Hafner, 1911.
(36) Hassan H. and Watany M. On Mathematical Analysis of Pavlovian Conditioning Learning Process using Artificial Neural Network Model, 10th Mediterranean Electro technical Conf., May 29-31, 2000, Cyprus.
(37) H. M. Hassan , and M. Watany. "On Comparative Evaluation And Analogy for Pavlovian and Throndikian Psycho-Learning Experimental Processes Using Bioinformatics Modeling", published at AUEJ, 6,3, 424-432, July. 2003.
(38) Hassan, M.H., 2008 " A Comparative Analogy of Quantified Learning Creativity in Humans Versus Behavioral Learning Performance in Animals: Cats, Dogs, Ants, and Rats.(A Conceptual Overview), published at WSSEC08 conference held on 18-22 August 2008, Derry, Northern
(39) H.M. Hassan, "On Mathematical Modeling of Cooperative E-Learning Performance During Face to Face Tutoring Sessions (Ant Colony System Approach)"published at IEEE EDUCON 2011,on Education Engineering–Learning Environments and Ecosystems in Engineering Education, held on April 4-6, 2011,Amman, Jordan. Available on line at: http://www.google.com.sa/url?sa=t&rct=j&q=&esrc=s&frm=1&source=web&cd=9&ved=0CHQQFjAI&url=http%3A%2F%2Feditlib.org%2Fd%2F45687&ei=rsUFU66SAc-c0wWakIHAAQ&usg=AFQjCNFXdog2WcQE_3DE5-8sVp7aaVH4Lw
(40) H. M. Hassan. “On Learning Performance Evaluation for Some Psycho-Learning Experimental Work versus an Optimal Swarm Intelligent System.", Published at ISSPIT 2005 (18-20 Dec.2005). http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1577175&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1577175
(41) H.M. Mustafa “A tutorial titled: Building up bridges for natural inspired computational models across behavioral brain functional phenomena; and open learning systems”, that has been presented at the International Conference on Digital Information and Communication Technology and its Applications (DICTAP 2011) held at Universite de Bourgogne, Dijon, France. (June 21-23, 2011). Available online at: http://dictap2011.sdiwc.us/tutorials.php
(42) Hassan M. H. Mustafa, and Fadhel Ben Tourkia “On Analysis and Evaluation of Learning Creativity Quantification via Naturally Neural Networks' Simulation and Realistic Modeling of Swarm Intelligence” published at the proceeding of the conference Eminent Association of Researchers in Engineering & Technology(EARET).To be held on 8-9 January 2018.
(43) Hassan M. H., et.al"On Comparative Analogy between Ant Colony Systems and Neural Networks Considering Behavioural Learning Performance" Journal of Computer Sciences and Applications, 2015, Vol. 3, No. 3, 79-89 Available online at http://pubs.sciepub.com/jcsa/3/3/4 © Science and Education Publishing DOI:10.12691/jcsa-3-3-4.
(44) Hassan M. H. "Analytical Comparison of Swarm Intelligence Optimization versus Behavioral Learning Concepts Adopted by Neural Networks (An Overview) American Journal of Educational Researchhttp://pubs.sciepub.com/education/3/7/2/index.html Vol. 3, No. 7, 2015, pp 800-806. doi: 0.12691/education-3-7-2
(45) Hassan M. H., et.al "Comparative Performance Analysis and Evaluation for One Selected Behavioral Learning System versus an Ant Colony Optimization System" Published at the Proceedings of the Second International Conference on Electrical, Electronics, Computer Engineering and their Applications (EECEA2015), Manila, Philippines, on Feb. 12-14, 2015.
(46) Hassan M. H., et.al. "On Assessment of Brain Function Adaptability in Open Learning Systems Using Neural Networks Modeling (Cognitive Styles Approach). Journal of American Science 2011; 7(9): 238-247]. (ISSN: 1545-1003). http://www.americanscience.org