|
RETE
(reet)
A
network especially of blood vessels or nerves
Common clues:
Nerve
network; Neural network; Anatomical network; Plexus; Network;
Bundle of nerves; Network of nerves; Blood vessel
network
Crossword
puzzle frequency:
once a year
Frequency
in English language:
67790 / 86800
Video: Introduction
to artificial neural networks
Computer
simulation of the branching architecture of the dendrites of
pyramidal neurons.
The
term neural network was traditionally used to refer to a network
or circuit of biological neurons. The modern usage of the term
often refers to artificial neural networks, which are composed of
artificial neurons or nodes. Thus the term has two distinct
usages:
1.
Biological neural networks are made up of real biological neurons
that are connected or functionally related in a nervous system.
In the field of neuroscience, they are often identified as groups
of neurons that perform a specific physiological function in
laboratory analysis.
Artificial
neural networks are composed of interconnecting artificial
neurons (programming constructs that mimic the properties of
biological neurons).
2.
Artificial neural networks may either be used to gain an
understanding of biological neural networks, or for solving
artificial intelligence problems without necessarily creating a
model of a real biological system. The real, biological nervous
system is highly complex: artificial neural network algorithms
attempt to abstract this complexity and focus on what may
hypothetically matter most from an information processing point
of view. Good performance (e.g. as measured by good predictive
ability, low generalization error), or performance mimicking
animal or human error patterns, can then be used as one source of
evidence towards supporting the hypothesis that the abstraction
really captured something important from the point of view of
information processing in the brain. Another incentive for these
abstractions is to reduce the amount of computation required to
simulate artificial neural networks, so as to allow one to
experiment with larger networks and train them on larger data
sets.
This
article is licensed under the GNU
Free Documentation License.
It uses material from the Wikipedia
article "Neural_network".
|
