Simulation of the Action Potential in the Neuron’s Membrane in Artificial Neural Networks - Advancing Artificial Intelligence Through Biological Process Applications - page 93

neurons or for developing methodologies that en-
able the estimation of the complexity of protein
structures (Balbín et al., 2007). It also could be
used for performing robotics advances based on
biological behaviours (Bermudez et al., 2007) or
for developing self-organising systems based on
biological characteristics, more specifically on
the three main characteristics of live organisms:
the multicellular architecture, the cellular divi-
sion and the cellular differentiation (Stauffer et
al., 2007). The same idea might be applicable
to design of multi-agent systems by using the
concepts of artificial life and biology (Menezes
et al., 2007)

of Favoured Races in the Struggle for Life . Cam-
bridge University Press, Cambridge, UK, sixth
edition. Originally published in 1859.

Freeman, J. A.. & Skapura, D. M. (1993). Neural
Networks . Ed. Addison-Wesley, USA.

Goldberg, D. E. (1989). Genetic Algorithms in
Search, Optimization and Machine Learning .
Addison-Wesley Reading, MA.

Haykin, S. (1999). Neural Networks: a Compre-
hensive Foundation . 2 nd Edition, PrenticeHall.

Hee Yeal & Sung Yan B. (1997). An Improved
Time Series Prediction by Applying the Layer-by-
Layer Learning Method to FIR Neural Networks.
Neural Networks . 10(9), 1717-1729.

REFERENCES

Holland, J. H. (1975). Adaptation in Natural
and Artificial Systems . University of Michigan
Press.

Almeida, F., Mateus, L., Costa, E., Harvey, I. &
Coutinho, A. (2007). Advances in Artificial Life .
LNAI 4648 . Springer, Berlin.

Johansson, E. M., Dowla, F. U. & Goodman, D. M.
(1992). Backpropagation learning for multi-layer
feed-forward neural networks using the conjugate
gradient method. International Journal of Neural
Systems , 2 (4), 291-301.

Angeline, P.J., Saunders, G.M. & Pollack, J.B.
(1994). An evolutionary algorithm that constructs
recurrent neural networks. IEEE Transactions on
Neural Networks , 5(1), 54-65.

Ku, K.W.C., Man, W.M. & Wan, C.S. (1999).
Adding learning to cellular genetic algorithms for
training recurrent neural networks. IEEE Transac-
tions on Neural Networks , 10(2), 239-252.

Balbín, A. & Andrade, E. (2007). Designing a
Methodology to Estimate Complexity of Protein
Structures. Advances in Artificial Life . LNAI 4648 .
Springer, Berlin.

Lippmann, R. P. (1987). An Introduction to
Computing with Neural Nets, IEEE ASSP Maga-
zine .

Bermudez, E. & Seth A.K. (2007). Simulations of
Simulations in Evolutionary Robotics. Advances
in Artificial Life . LNAI 4648 . Springer, Berlin.

Mackey M. & Glass L. (1977). Oscillation and
chaos in physiological control systems. Science ,
197-287.

Brion, G.M. and Lingireddy, S. (1999). A neural
network approach to identifying nonpoint sources
of microbial contamination. Water Res.,33(14),
3099-3106.

Menezes, T. & Costa, E. (2007). Designing for
Surprise. Advances in Artificial Life . LNAI 4648 .
Springer, Berlin.

Box, G. E. & Jenkins, G. M. (1976). Time Series
Analysis Forecasting and Control. Ed. Holden
Day.

Pearlmutter, B. A. (1990). Dynamic Recurrent
Neural Networks. Technical Report CMU-CS ,
School of Computer Science, Carnegie Mellon
University, 88-191.

Darwin, C. (1864). On the Origin of Species by
means of Natural Selection or the Preservation