Artificial neural network with modulation of synaptic coefficients
Journal of Samara State Technical University, Ser. Physical and Mathematical Sciences, Tome 131 (2013) no. 2, pp. 58-71 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

The model of neural network based on artificial neuron with dynamic synaptic weights was constructed. As main model processes for changing the synaptic weights were chosen: weakening of a synaptic weight in the absence of synapse stimulation, and modulation of a weight with synchronous irritation of some other synaptic junction.
Keywords: artificial neuron with synaptic plasticity.
@article{VSGTU_2013_131_2_a6,
     author = {M. N. Nazarov},
     title = {Artificial neural network with modulation of~synaptic coefficients},
     journal = {Journal of Samara State Technical University, Ser. Physical and Mathematical Sciences},
     pages = {58--71},
     year = {2013},
     volume = {131},
     number = {2},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VSGTU_2013_131_2_a6/}
}
TY  - JOUR
AU  - M. N. Nazarov
TI  - Artificial neural network with modulation of synaptic coefficients
JO  - Journal of Samara State Technical University, Ser. Physical and Mathematical Sciences
PY  - 2013
SP  - 58
EP  - 71
VL  - 131
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/VSGTU_2013_131_2_a6/
LA  - ru
ID  - VSGTU_2013_131_2_a6
ER  - 
%0 Journal Article
%A M. N. Nazarov
%T Artificial neural network with modulation of synaptic coefficients
%J Journal of Samara State Technical University, Ser. Physical and Mathematical Sciences
%D 2013
%P 58-71
%V 131
%N 2
%U http://geodesic.mathdoc.fr/item/VSGTU_2013_131_2_a6/
%G ru
%F VSGTU_2013_131_2_a6
M. N. Nazarov. Artificial neural network with modulation of synaptic coefficients. Journal of Samara State Technical University, Ser. Physical and Mathematical Sciences, Tome 131 (2013) no. 2, pp. 58-71. http://geodesic.mathdoc.fr/item/VSGTU_2013_131_2_a6/

[1] Yu. F. Golubev, “Neural networks in mechatronics”, J. Math. Sci., 147:2 (2007), 6607–6622 | DOI | MR | Zbl

[2] P. D. Wasserman, Neural Computing, theory and practice, Van Nostrand Reinhold, New York, 1989; F. Uossermen, Neirokompyuternaya tekhnika: Teoriya i praktika, Mir, M., 1992, 240 pp.

[3] T. Kohonen, Self-Organizing Maps. Third extended edition, Springer Series in Information Sciences, 30, Springer-Verlag, Berlin, 2001, xx+501 pp. | DOI | Zbl

[4] A. L. Hodgkin, A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve”, J. Physiol., 1952, no. 117, 500–544

[5] V. V. Maiorov, I. Yu. Myshkin, “Mathematical modeling of a neuron net on the basis of the equation with delays”, Matem. Mod., 2:11 (1990), 64–76 | MR | Zbl

[6] O. A. Dunaeva, “Principles of constructing layered neural networks based on pulse neurons”, Model. Anal. Inform. Sist., 18:2 (2011), 65–76

[7] E. V. Konovalov, “The problem of adaptation of the generalized neural element”, Model. Anal. Inform. Sist., 19:1 (2012), 69–83

[8] J.-H. Han, S. A. Kushner, A. P. Yiu, C. J. Cole, A. Matynia, R. A. Brown, R. L. Neve, J. F. Guzowski, A. J. Silva, S. A. Josselyn, “Neuronal Competition and Selection During Memory Formation”, Science, 316:5823 (2007), 457–460 | DOI

[9] I. Antonov, I. Antonova, E. R. Kandel, R. D. Hawkinssend, “Activity-Dependent Presynaptic Facilitation and Hebbian LTP Are Both Required and Interact during Classical Conditioning in Aplysia”, Neuron, 37:1 (2003), 135–147 | DOI