Least Action Trajectory in Neural Networks


The study of complex networks had developed over the years to include systems such as traffic, predator-prey interactions, financial market, and even the world wide web. Complex network studies encompass biology, chemistry, physics, and even engineering and economics [1-6]. However, the dynamics of such complex networks are yet to be understood fully [7,8]. In this paper, we will be focusing mostly on the possible learning ability in a complex network. To do this, an optimization process is used via Wiener process [9,10]. It is apparent from the sample lattice shown that the final position was not a basis of the transition probability, or it was never used to calculate the probability, since the transition probability only considers the current position. The final point is reached because of the orientation of the edges, where each edge is facing the final point, an aspect of the nervous system (afferent and efferent nerves) [11-13]. No matter how random the orientation of the neurons is, each directs to the central nervous system for processing and is transmitted away for reaction.

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E. C. Castro and B. Anne R. Pelicano, "Least Action Trajectory in Neural Networks," Open Journal of Applied Sciences, Vol. 3 No. 3B, 2013, pp. 6-8. doi: 10.4236/ojapps.2013.33B002.

Conflicts of Interest

The authors declare no conflicts of interest.


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