How the Brain Works

lovethreewayAI and Robotics

Oct 20, 2013 (3 years and 5 months ago)


How are Artificial Neural Networks like the real biological brain?

How the
Brain Works

The human brain has close to 100 billion nerve cells, called neurons. Each neuron is
connected to thousands of others, creating a neural network that shuttles information in
the form of stimuli, in and out of the brain constantly.

Each neuron is made up of four main parts: the synapses, soma, axon, and dendrites.
The soma is the

body of the cell where the information is processed. Each neuron has long, thin nerve
fibers called dendrites that bring information in and even longer f
ibers called axons that
send information away. The neuron receives information in the form of electrical signals
from neighboring neurons across one of thousands of synapses, small gaps that separate
two neurons and act as input channels.

Once a neuron ha
s received this charge it triggers either a "go" signal that allows the
message to be passed to the next neuron or a "stop" signal that prevents the message
from being forwarded. When a person thinks of something, sees an image, or smells a
scent, that men
tal process or sensory stimulus excites a neuron, which fires an electrical
pulse that shoots out through the axons and fires across the synapse. If enough input is
How are Artificial Neural Networks like the real biological brain?

received at the same time, the neuron is activated to send out a signal to be picked up by
the next neuron's dendrites. Most of the brain consists of the "wiring" between the
neurons, which makes up one thousand trillion connections. If these fibers were real
wire, they would measure out to an estimated 63,140 miles inside the average skull.

ch stimulus leads to a chain reaction of electrical impulses, and the brain is constantly
firing and rewiring itself. When neurons repeatedly fire in a particular pattern, that
pattern becomes a semipermanent feature of the brain. Learning comes when patte
are strengthened, but if connections are not stimulated, they are weakened. For
example, the more a student repeats the number to open a combination lock, the more
the connections that take in that information are bolstered to create a stronger memory
that will be easily retrieved the next time. At the end of the school year, when a student
puts the lock away, that number will not be used for a couple of months. Those three
numbers will be much harder to recall when fall comes and that student needs to
the lock again.

Artificial Neural Networks

The branch of AI that modeled its work after the neural network of the human brain is
called connectionism. It is based on the belief that the way the brain works is all about
making the right connections,
and those connections can just as easily be made using
silicon and wire as living neurons and dendrites.

Called artificial neural networks (ANNs), these programs work in the same way as the
brain's neural network. An artificial neuron has a number of conn
ections or inputs. To
mimic a real neuron, each input is weighted with a fraction between 0 and 1. The weight
indicates how important the incoming signal for that input is going to be. An input
weighted 0.4 is more important than an input weighted 0.1. All

of the incoming signals'
weights are added together and the total sum equals the net value of the neuron.

Each artificial neuron is also given a number that represents the threshold or point over
which the artificial neuron will fire and send on the sign
al to another neuron. If the net
value is greater than the threshold, the neuron will fire. If the value is less than the
threshold, it will not fire. The output from the firing is then passed on to other neurons
How are Artificial Neural Networks like the real biological brain?

that are weighted as well. For example, the

computer's goal is to answer the question,
Will the teacher give a quiz on Friday? To help answer the question, the programmer
provides these weighted inputs:

The teacher loves giving quizzes = 0.2.

The teacher has not given a quiz in two weeks = 0.1.

The teacher gave the last three quizzes on Fridays = 0.3.

The sum of the input weights equals 0.6. The threshold assigned to that neuron is 0.5. In
this case, the net value of the neuron exceeds the threshold number so the artificial
neuron is fired. This

process occurs again and again in rapid succession until the process
is completed.

If the ANN is wrong, and the teacher does not give a quiz on Friday, then the weights are
lowered. Each time a correct connection is made, the weight is increased. The nex
t time
the question is asked, the ANN will be more likely to answer correctly. The proper
connections are weighted so that there is more chance that the machine will choose that
connection the next time. After hundreds of repeated training processes, the c
neural network connections are strengthened and remembered, just like a memory in
the human brain. This is how the ANN is trained rather than programmed with specific
information. A well
trained ANN is said to be able to learn. In this way the compu
ter is
learning much like a child learns, through trial and error. Unlike a child, however, a
computer can make millions of trial
error attempts at lightning speed.

How are Artificial Neural Networks like the real biological brain?

Whereas traditional AI expert systems are specialized and inflexible, ANN systems ar
trainable and more flexible, and they can deal with a wide range of data and
information. They can also learn from their mistakes. This kind of AI is best for
analyzing and recognizing patterns.

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