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| Computers Forum Index » Computer Artificial Intelligence - Neural Nets » Into the principle of neural network operation... |
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| Author |
Message |
| Oleg Volkov... |
 Posted: Mon Nov 02, 2009 10:13 am |
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Guest
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Hi, all!
I want to share with you ideas I have got of how a neural network
within a
biological system can operate. I do not know whether the ideas are
new,
however.
I am not well informed about current trends in development of
artificial
neural networks. From the information that catches my eye when I'm
surfing
the Internet I came to conclusion that modern algorithms of training
of
neural networks often imply the process of training can not be
performed
by neural network itself and requires computational facilities outside
the
network. Some training algorithms even require the use of function
gradient, so the question arises whether these complexities can really
be
encountered in biological systems. The complexities may exist, but
they
should rest upon simple and sound principles, which are mostly used by
nature.
I came across with the use of function gradient when reading the
abstract
of an article in which Hidden Markov Models (HMMs) were suggested to
be
among the concepts biological neural networks are based on. The idea
of
state model of some sort represented by biological neural network is
not
new and is proved by experiments in which individual neurons in human
or
animal brain are activated when specific pictures are exposed to
living
beings or when those beings find themselves in specific situations.
So one can consider the objective of operation of biological neural
network
is the building of environment state model and acting according to
that
model. The whole meaning of life is inconceivable, but at the
beginning, we
can think the task living being should certainly solve is to obtain
food
from the environment, or some kind of resource, or energy.
To model the environment as a state system a deterministic finite
automaton
can be taken as initial approximation. The neural network has to
communicate with the automaton by means of automaton input and output
signals. To some transitions between automaton states numbers can be
assigned which indicate the amount of food, resource, or energy living
being receives when corresponding transition is made. Therefore, the
neural
network should explore the model of automaton states and make the
automaton
to execute transitions with greater numbers more often.
I do not know whether the algorithm occurred to me is new. Regardless
of
that fact, the software project I am giving a link to is of
indispensable
benefit it contains the algorithm implemented programmatically. The
basic
idea of the algorithm is quite simple and therefore can be discovered
in
biological neural networks some day. Roughly speaking, the main part
of the
algorithm is dedicated to solving two tasks simultaneously: state
model
training and a kind of task usually performed by Viterbi algorithm.
But... The algorithm description is still draft and possibly contains
wrong, ungrounded, or unclear statements. Nevertheless, I hope reading
the
description will be helpful to you.
QSMM Project pages:
http://qsmm.sourceforge.net/
http://sourceforge.net/projects/qsmm/
Oleg Volkov |
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