artificial neuron

First the Artificial neurons must be able to work together in network just like real neurons.

Nodes or units are simple processing elements, which can be considered artificial neurons.

Artificial neurons bear a striking similarity to their biological counterparts.

They need only study the brain's neural structure and join artificial neurons to form the same functional pattern.

A Neural network is an artificial system (made of artificial neuron cells).

This is in contrast to the artificial neuron, which aims for computational effectiveness, although these goals sometimes overlap.

As for the first meaning, the artificial neurons and synapses in hybrid networks can be digital or analog.

Artificial neurons are the constitutive units in an artificial neural network.

An artificial neural network is a network of artificial neurons.

Each of the artificial neurons operates in a far more orderly manner than biological neurons do.

Unlike most artificial neurons, however, biological neurons fire in discrete pulses.

They showed theoretically that networks of artificial neurons could implement logical, arithmetic, and symbolic functions.

Threshold value of an artificial neuron.

Each model cell (see artificial neuron) corresponds to a small population of neurons which are located at close range and which fire together.

An artificial neuron is a mathematical function conceived as a crude model, or abstraction of biological neurons.

Simplified models of biological neurons were set up, now usually called perceptrons or artificial neurons.

These relationships result in simplified implementations of artificial neural networks with artificial neurons.

Neurons with this kind of activation function are also called artificial neurons or linear threshold units.

All point processes, including those standing for cells or synapses artificial neurons, and all graphs operate in this "run" time.

Artificial neuron, also called a "semi-linear unit"

The artificial neuron transfer function should not be confused with a linear system's transfer function.

A set of artificial neurons learn to map points in an input space to coordinates in an output space.

The neural network people take a bottom-up approach: they assemble a network of artificial neurons and hope that a mind will emerge.

Modeling design depends on whether it is artificial neuron or biological neuron of neuronal model.

Backpropagation requires that the activation function used by the artificial neurons (or "nodes") be differentiable.