TITLE:
Least Action Trajectory in Neural Networks
AUTHORS:
Ellison C. Castro, Bhazel Anne R. Pelicano
KEYWORDS:
Neural Networks; Optimization; Wiener process
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.3 No.3B,
October
10,
2013
ABSTRACT: 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.