Modeling learning on dynamic behaviour of synapses

• Autorzy: Fernando S. , Cho Y. I. , Nakamura Y. , Matsuzaki S. , Marasinghe A.
• Języki publikacji: EN

Abstrakty

EN

Learning is a process involved in multiple timescales. As per biology, changes which last from milliseconds to seconds and hours to days are the main mediators for the formation of short-term and long-term memory. It is obvious that, memory formation is neither static nor it is restricted into a one phase of life. Every step we keep in our life, even it succeed or fail or no matter what happen, we learn from them and acquire invaluable knowledge on that, which makes us easy manipulation on similar events in future. Thus continuous learning in a dynamic environment is a necessary qualification for the researches which are interested in studying phenomena, such as addiction, stress, noise, etc on such a dynamic learning environments. This research proposes a new approach of modelling our nervous system with the intention of implementing learning on dynamic environment.

Słowa kluczowe

EN

learning; short-term plasticity; long-term plasticity; agent systems

PL

nauczanie; krótkoterminowa plastyczność; długoterminowa plastyczność; plastyczność synaptyczna

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2009
• Tom: Vol. 13
• Strony: 175--180
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Fernando S.

• Department of Management and Information Systems Engineering, Nagaoka University of Technology, Japan

autor

Cho Y. I.

autor

Nakamura Y.

autor

Matsuzaki S.

autor

Marasinghe A.

Bibliografia

• [1] ABBOTT L.F., NELSON S.B., Synaptic plasticity: taming the beast, Neuroscience, Nature,Vol.3. pp-1178-1183, 2000.
• [2] ABBOTT L.F., REGEHR W.G., Synaptic computation, Nature, Vol.431, pp. 796-803, 2004.
• [3] BURRONE J., MURTHY V.N., Synaptic gain control and homeostasis, Current Opinion in Neurobiology, Vol.13. pp. 560-567, 2003.
• [4] DAVIS G.W., Homeostatic control of neural activity: from phenomenology to molecular design, Annual Review of Neuroscience, Vol. 29. pp. 307-323, 2006.
• [5] DESTEXHE A., MARDER E., Plasticity in single neuron circuit computations, Nature, Vol. 431, pp. 789-795, 2004.
• [6] HYMAN S.E., MALENKA R.C., Addiction and the brain: the neurobiology of compulsion and its persistence, Neuroscience, Nature, Vol.2. pp. 695-703, 2001.
• [7] MAASS W., ZADOR M., Dynamic stochastic synapses as computational units, MIT Press, 1998.
• [8] TURRIGIANO G., Homeostatic plasticity in neural networks: the more things change, the more they stay the same, Trends in Neuroscience, Elsevier Science, Vol.22.No.5, pp. 221-227. 1999.
• [9] TURRIGIANO G., NELSON S., Homeostatic plasticity in the developing nervous system, Neuroscience, Nature, Vol.5. pp. 97-107, 2004.
• [10] ZUCKER R., Short-term synaptic plasticity, Annual Review of Neuroscience, Vol.12, pp. 13-31, 1989.
• [11] Genetic Science Learning Center, Drugs Alter the Brain's Reward Pathway, Learn. Genetics 11 August 2009 http://learn.genetics.utah.edu/content/addiction/drugs/
• [12] American Council of Drug Education, basic facts about drugs: Marijuana, 11 August 2009, http://www.acde.org/common/Marijana.htm