Open Journal of Applied Sciences, 2013, 3, 6-8 Published Online July 2013 (
Copyright © 2013 SciRes. OJAppS
Least Action Trajectory in Neural Networks
Ellison C. Castro*, Bhazel Anne R. Pelicano
Department of Physical Sciences, University of the Philippines, Baguio City, Philippines
Email: *
Received July 2013
The study of complex networks had developed over the years to include systems such as traffic, predator-prey interac-
tions, 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 po ssible 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 transi-
tion 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.
Keywords: Neura l Ne t works; Optimization; Wiene r process
1. Introduction
Complex networks had been heavily used to model sys-
tems such as traffic [1-4], bankruptcy [14], social net-
working [15,16], and protein-protein interaction [17,18].
While some of these systems are random in nature [19],
others, such as human interactions (e.g. social networks)
have learning capabilities. This behavior is similar to the
brain [20], wherein, signals traveling from one neuron to
another may opt to travel at the optimal path.
Finding the optimized path often leads us to the La-
grange equations of motion for classical dynamics [21].
However, quantum mechanics often resolve to take on
optimization using path integral techniques [10], which,
in principle, is simil a r to La g rangian dynamic s .
Hamiltons Principle and Lagrange equations of mo-
tion have been widely used to study the extremized,
usually minimized, behavior and properties of mechani-
cal systems (e.g. brachistochrone problem) [21]. Howev-
er, the extremization of physical systems has been li-
mited to classical systems. Several studies and researches
have been reported of using the Lagrange and Hamilto-
nian dynamics to chaotic and stochastic systems [22 ,23].
In this paper, the extremized or least action path of
Brownian is numerically simulated, to model propagation
of neuronal signals.
2. Methodology
Before the numerical computation of the least-action
trajectory of the Brownian motion, the lattices (or the
directed graphs) where the Brownian particle moves, are
defined. To graphically show a regular lattice, each ho-
rizontal and vertical line is plotted within a for loop f un c-
tion, where the size and spacing of the lattice are con-
cerned. As for the irregular lattice, each line shown is
manually inputted. These methods show visual guides of
the trajectory of the Brownian motion and do not affect
the result.
Regardless of the type of lattice, each of them follows
a form which defines each node and direction of the edge
into the numerical computation. The nodes can be
represented by DG{a, b} = [x, y], where DG is a matrix
which contains two-element row matrices, having the
elements x and y, as its elements. The position of the row
matrices is defined by a and b as the row and column
position, respectively. The in terpretation s of the variab les
of DG are as follows: the variable a is the current node
position of th e Brownian particle, while b is the node the
Brownian particle can reach if it is in node a. The va-
riables x and y are the Cartesian coordinates of node b.
This definition of the coordinates of node b will be useful
numeric-wise and graphic-wise. In summary, if DG{a, b}
is not nil, then the Brownian particle at node a can go to
node b.
In the numerical process, the particle starts at node 1
for convenience. The program will search through all
DG{1, b} and pick all the elements having a value. Since
the values are coordinates, they can be used to calculate
Corresponding author.
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the transition probability of the particle.
( )
( )
( )
WDt tDt t
= −
The equation shows the transition probability of the
Brownian particle in two dimensions, where x = (x1, x2)
2 22
xxx= +
, and D is the diffusion coefficient of
the fluid. The time it takes for the particle to reach a spe-
cific position (i.e. t ˗ t0) is assumed to be unity. Each DG
that has a value will have a corresponding transition
probability. These probabilities are then stored in a sto-
chastic matrix for normalization. The columns of the
stochastic matrix correspond to the Brownian particle’s
initial node position, whereas the rows correspond to the
possible future positions of the particle. From here, the
comparison of the probabilities will start in a specific
column and then through the rows of the column. This
will return the element position having the highest prob-
ability, which in turn can be translated into the next cur-
rent node. The process repeats until the Brownian par-
ticle reaches the final node and then the whole process is
redone for Monte Carlo simulation.
3. Results
To model a more realistic neural network, an irregular
design is used (e.g. Figure 1). This is a directed graph of
a neural network where each node represents the cell
body and each directed edge defines the orientation of
axons (the signal can be transmitted in the direction of
the arrow).
In the optimized path of the signal (Figure 2), the path
do not pass through points other than the almost hori-
zontal line connecting the initial and fina l position.
It is apparent from both lattices (Figures 2 and 3) that
Figure 1. Example of a directed graph for an irregular lat-
Figure 2. Optimal path for an irregular lattice.
Figure 3. Simplified optimal path from the cortex to the
the final position was not a basis of the transition proba-
bility, nor was it used to calculate the probability. This is
because the transition probability only considers the cur-
rent position, which means there is no correlation be-
tween “steps” as the signal moves from node to node.
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, each directs to the central nervous system for
processing and is transmitted away for reaction.
The study by De Marco Garcia, et al., on the identifi-
cation of the muscle nerve trajectory has shown that sig-
nals, differentiated by dyes, do not diffuse onto all net-
works [24]. This model may be able to simulate what is
going on in the said-system.
4. Summary and Conclusions
Using the dependence of the Brownian motions action
Copyright © 2013 SciRes. OJAppS
to the transition probability, the trajectory of least action
for the motion can be described. The optimized path in
free space does not depend on the frequency of the par-
ticle to pass through a certain point, but depends on the
most probable point in space in relation to the particles
current position. Even though the transition probability is
independent of the history of the Brownian motion, the
path of least action is indirectly affected by the initial
Brownian motion in a neural network still has a clas-
sical characteristic, i.e. the trajectory most likely follows
a straight line fro m the starting position to the d esignated
point. Whereas, the installation of the cond ition of a final
point never appears in the transition probability calcula-
tion. It appears to be that it is inherent to the transition
probability itself that the Brownian motion is subject to
follow a horizontal of vertical displacement. The final
position can then be reached if the Brownian motion ap-
proaches the point, which can be done by orienting the
edges of t he directed graph.
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