Higher-Order Computational Model for Novel Neurons - High Dimensional Neurocomputing: Growth, Appraisal and Applications - page 102

3. Arcas, B.A., Fairhall, A.L., Bialek, W.: What can a single neuron compute? In: Leen, T.,
Dietterich, T., Tresp, V. (eds.) Advances in Neural Information Processing, pp. 75-81. MIT
press, Cambridge (2001)
4. McCulloch, W.S., Pitts, W.: A logical calculation of the ideas immanent in nervous activity.
Bull. Math. Biophys. 5 , 115-133 (1943)
5. Koch, C.: Biophysics of Computation: Information Processing in Single Neurons. Oxford
University Press, New York (1999)
6. Mel, B.W., Koch, C.: Sigma-pi learning : on radial basis functions and cortical associative
learning. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems 2, pp.
474-481. Morgan-Kaufmann, San Mateo, CA (1990)
7. Durbin, R., Rumelhart, R.: Product units: a computationally powerful and biologically plausible
extension to backpropagation networks. Neural Comput. 1 , 133-142 (1989)
8. Bukovsky, I., Bila, J., Gupta, M.M., Hou, Z.G., Homma, N.: Foundation and classification of
nonconventional neural units and paradigm of nonsynaptic neural interaction. In: Wang, Y.
(ed.) (University of Calgary, Canada) Discoveries and Breakthroughs in Cognitive Informatics
and Natural Intelligence (in the ACINI book series). IGI, Hershey PA, USA (ISBN: 978-1-
60566-902-1) (2009)
9. Taylor, J.G., Commbes, S.: Learning higher order correlations. Neural Netw. 6 , 423-428 (1993)
10. Cotter, N.E.: The Stone-Weierstrass theorem and its application to neural networks. IEEE
Trans. Neural Netw. 1 , 290-295 (1990)
11. Shin, Y., Ghosh, J.: The Pi-sigma Network: an efficient higher-order neural network for pattern
classification and function approximation. Proceedings of the International Joint Conference
on Neural Networks, pp. 13-18 (1991)
12. Heywood, M., Noakes, P.: A framework for improved training of Sigma-Pi networks. IEEE
Trans. Neural Netw. 6 , 893-903 (1996)
13. Chen, M.S., Manry, M.T.: Conventional modeling of the multilayer perceptron using polyno-
mial basis functions. IEEE Trans. Neural Netw. 4 , 164-166 (1993)
14. Anthony, A., Holden, S.B.: Quantifying generalization in linearly weighted neural networks.
Complex Syst. 18 , 91-114 (1994)
15. Chen, S., Billings, S.A.: Neural networks for nonlinear dynamic system modeling and identi-
fication. Int. J. Contr. 56 (2), 319-346 (1992)
16. Schmidt, W., Davis, J.: Pattern recognition properties of various feature spaces for higher order
neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 15 , 795-801 (1993)
17. Kosmatopoulos, E., Polycarpou,M., Christodoulou,M., Ioannou, P.: High-order neural network
structures for identification of dynamical systems. IEEE Trans. Neural Netw. 6 (2), 422-431
(1995)
18. Liu, G.P., Kadirkamanathan, V., Billings, S.A.: On-line identification of nonlinear systems
using volterra polynomial basis function neural networks. Neural Netw. 11 (9), 1645-1657
(1998)
19. Elder, J.F., Brown D.E.: Induction and polynomial networks. In: Fraser, M.D. (ed.) Network
Models for Control and Processing, pp. 143-198. Intellect Books, Exeter, UK (2000)
20. Bukovsky, I., Redlapalli, S., Gupta, M.M.: Quadratic and cubic neural units for identification
and fast state feedback control of unknown non-linear dynamic systems. Fourth International
Symposium on UncertaintyModeling and Analysis ISUMA 2003, pp. 330-334 (ISBN 0-7695-
1997-0). IEEE Computer Society, Maryland, USA (2003)
21. Hou, Z.G., Song, K.Y., Gupta, M.M., Tan, M.: Neural units with higher-order synaptic opera-
tions for robotic image processing applications. Soft Comput. 11 (3), 221-228 (2007)
22. Nikolaev, N.Y., Iba, H.: Adaptive Learning of Polynomial Networks: Genetic Programming,
Backpropagation and Bayesian Methods (ISBN: 0-387-31239-0, series: Genetic and Evolu-
tionary Computation), vol. XIV, p. 316. Springer, New York (2006)
23. Zhang, M. (ed.) (Christopher Newport University): Artificial Higher Order Neural Networks
for Economics and Business (ISBN: 978-1-59904-897-0). IGI-Global, Hershey, USA (2008)
24. Rosenblatt, F.: The perceptron : a probabilistic model for information storage and organization
in the brain. Psychol. Rev. 65 , 231-237 (1958)