W. S. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, vol.5, issue.4, pp.115-133, 1943.
DOI : 10.1007/BF02478259

D. Hebb, The Organization of Behavior, 1949.

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.386-408, 1958.
DOI : 10.1037/h0042519

F. Rosenblatt, Principles of Neurodynamics, 1962.

B. Widrow and M. Hoff, Adaptive switching circuits, 1960.
DOI : 10.21236/AD0241531

M. L. Minsky and S. Papert, Perceptrons: An Introduction to Computational Geometry, 1969.

A. H. Klopf, Brain Function and Adaptive Systems -A Heterostatic Theory, pp.72-0164, 1972.

K. Fukushima, Cognitron: A self-organizing multilayered neural network, Biological Cybernetics, vol.176, issue.3-4, pp.121-136, 1975.
DOI : 10.1113/jphysiol.1969.sp008820

S. Grossberg, Adaptive Pattern Classification and Universal Recoding, Biol. Cyber, vol.233, pp.121-134, 1976.
DOI : 10.1007/978-94-009-7758-7_12

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

J. J. Hopfield, Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proc. Natl. Acad. Sci. USA, p.79, 1982.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning internal representations by error propagation. in: Parallel distributed processing: explorations in the microstructure of cognition, pp.318-362, 1986.

G. A. Carpenter and S. Grossberg, A massively parallel architecture for a self-organizing neural pattern recognition machine, Computer Vision, Graphics, and Image Processing, vol.37, issue.1, pp.54-115, 1987.
DOI : 10.1016/S0734-189X(87)80014-2

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, vol.61, pp.85-117, 2014.
DOI : 10.1016/j.neunet.2014.09.003

G. Sheela, K. Deepa, and S. N. , Review on Methods to Fix Number of Hidden Neurons in Neural Networks, Mathematical Problems in Engineering, vol.11, issue.3, 2013.
DOI : 10.1016/S0893-6080(97)00111-1