Model of a Neuron Network in Human Brains for Learning Assistance in E-Learning Environments

Harald R. Kisch, Claudia L. R. Motta

Abstract

It is typically known that brain neurons are responsible for a significant part of our knowledge adaption process. However, it is not yet fully understood how knowledge adaption works or what conscious intelligence is. The aim of this research is to investigate how an E-learning environment can automatically identify learning sequences to dynamically map them to specific learning types for suggesting course material, which makes learning more individual, flexible and faster. For the purpose of this research, a neural ontology is created. In this ontology, the characterization of one neural brain cell is meant to represent every neuron cell in our brain as a specific part of a neural network to get closer to the answer how a simulation of brain functions could be accomplished. This paper describes a neural network theory and how the conceptual model of a neural brain cell could be interpreted through the concept of cognitive pattern match in relation to intelligence. In conclusion, two fundamental hypotheses for effective knowledge adaption in E-learning environments are derived.

References

  1. Falbo, R. A. (2004). Experiences in using a method for building domain ontologies. Proc. of International Workshop on Ontology In Action, Banff, Alberta, Canada.
  2. Fishwick, P. and Miller, J. (Jan. 2004). Winter SImulation Conference, Proceedings of The 2004. IEEE, Piscataway.
  3. Grubb, M. S. and Burrone, J. (2010). Activity-dependent relocation of the axon initial segment fine-tunes neuronal excitability. 465(7301), pages 1070-1074. Nature.
  4. Guizzardi and Wagner (op. 2010). Towards an ontological foundation of discrete event simulation. In Proceedings of the 2010 Winter Simulation Conference, eds. B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Ycesan, 652-664. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc. IEEE, Piscataway.
  5. Guizzardi, G. (2005). Ontological foundations for structural conceptual models, volume no. 015 of Telematica Instituut fundamental research series. Centre for Telematics and Information Technology and Telematica Instituut, Enschede and Enschede.
  6. Guizzardi, G. (2009). The problem of transitivity of partwhole relations in conceptual modeling revisited. Federal University of Espirito Santo (UFES).
  7. Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. A Wiley book in clinical psychology. Wiley [u.a.], New York u.a.
  8. Mosteghanemi, H. and Drias, H. (2012). Bees swarm optimization for real time ontology based information retrieval. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on, volume 3, pages 154-158.
  9. Nan-Jie Xu, S. S. et al. (2011). A dual shaping mechanism for postsynaptic ephrin-b3 as a receptor that sculpts dendrites and synapses. Nature Neuroscience.
  10. Poli, R., Healy, M., and Kameas, A. (2010], c 2010). Theory and applications of ontology. Springer, Dordrecht and London and New York.
  11. Rybak, J. et al. (2010). The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System. Frontiers Research Foundation.
  12. Sanders, W. (1946). Conduction velocity and myelin thickness in regenerating nerve fibres. Department of Physiology and Zoology and Comparative Anatomy.
  13. Silver et al. (op. 2009). Supporting Interoperability Using the Discrete-Event Modeling Ontology (DeMo). In Proceedings of the 2009 Winter Simulation Conference, eds. M. D. Rosetti, R. R. Hill, B. Johansson, A. Dunkin and R. G. Ingalls, 1399-1410. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc. IEEE, Piscataway.
  14. Sporns, O. (2011). Networks of the brain. MIT Press, Cambridge and Mass.
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Paper Citation


in Harvard Style

R. Kisch H. and L. R. Motta C. (2015). Model of a Neuron Network in Human Brains for Learning Assistance in E-Learning Environments . In Proceedings of the 7th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-108-3, pages 407-415. DOI: 10.5220/0005439904070415


in Bibtex Style

@conference{csedu15,
author={Harald R. Kisch and Claudia L. R. Motta},
title={Model of a Neuron Network in Human Brains for Learning Assistance in E-Learning Environments},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2015},
pages={407-415},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005439904070415},
isbn={978-989-758-108-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Model of a Neuron Network in Human Brains for Learning Assistance in E-Learning Environments
SN - 978-989-758-108-3
AU - R. Kisch H.
AU - L. R. Motta C.
PY - 2015
SP - 407
EP - 415
DO - 10.5220/0005439904070415