The DICT Development Group
4 definitions found
From The Collaborative International Dictionary of English v.0.48 :
Neuron \Neu"ron\, n.; pl. Neura. [NL., from Gr. ney^ron
1. The brain and spinal cord; the cerebro-spinal axis;
myelencephalon. [obsolete] --B. G. Wilder.
2. (Cell Biology) The characteristic specialized cell that is
part of the nervous system, serving to conduct electrical
impulses to and from the brain, and also between other
parts of the body, and composed of a main cell body, the
axon, with a varying number of processes of varying
length, the dendrites; a nerve cell. The movement and
behavior of higher animals depends on the signals
tranmsitted by such nerve cells.
From WordNet (r) 3.0 (2006) :
n 1: a cell that is specialized to conduct nerve impulses [syn:
nerve cell, neuron]
From Moby Thesaurus II by Grady Ward, 1.0 :
27 Moby Thesaurus words for "neuron":
afferent neuron, autonomic nervous system, axon, brain,
central nervous system, cerebral cortex,
craniosacral nervous system, dendrite, effector organ,
efferent neuron, ganglion, gray matter, internuncial neuron,
medullary sheath, nerve, nerve trunk, nervous system,
peripheral nervous system, plexus, sensorium, sensory area,
sensory cell, solar plexus, spinal cord, synapse,
thoracolumbar nervous system, white matter
From The Free On-line Dictionary of Computing (30 December 2018) :
artificial neural network
(ANN, commonly just "neural network"
or "neural net") A network of many very simple processors
("units" or "neurons"), each possibly having a (small amount
of) local memory. The units are connected by unidirectional
communication channels ("connections"), which carry numeric
(as opposed to symbolic) data. The units operate only on
their local data and on the inputs they receive via the
A neural network is a processing device, either an
algorithm, or actual hardware, whose design was inspired by
the design and functioning of animal brains and components
Most neural networks have some sort of "training" rule whereby
the weights of connections are adjusted on the basis of
presented patterns. In other words, neural networks "learn"
from examples, just like children learn to recognise dogs from
examples of dogs, and exhibit some structural capability for
Neurons are often elementary non-linear signal processors (in
the limit they are simple threshold discriminators). Another
feature of NNs which distinguishes them from other computing
devices is a high degree of interconnection which allows a
high degree of parallelism. Further, there is no idle memory
containing data and programs, but rather each neuron is
pre-programmed and continuously active.
The term "neural net" should logically, but in common usage
never does, also include biological neural networks, whose
elementary structures are far more complicated than the
mathematical models used for ANNs.
See Aspirin, Hopfield network, McCulloch-Pitts neuron.
Usenet newsgroup: news:comp.ai.neural-nets.
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