The Collaborative International Dictionary of English v.0.48:

Neuron \Neu"ron\, n.; pl. Neura. [NL., from Gr. ney^ron
   nerve.] (Anat.)
   1. The brain and spinal cord; the cerebro-spinal axis;
      myelencephalon. [obsolete] --B. G. Wilder.
      [1913 Webster]

   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.
      [PJC]

WordNet (r) 3.0 (2006):

neuron
    n 1: a cell that is specialized to conduct nerve impulses [syn:
         nerve cell, neuron]

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



The Free On-line Dictionary of Computing (30 December 2018):

artificial neural network
neural nets
neural network
neuron
NN

    (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
   connections.

   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
   thereof.

   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
   generalisation.

   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.

   (1997-10-13)