Artificial Neural Networks: Concepts

A highly complex, nonlinear, and parallel computer that can organize its structural constituents (neurons) to perform computations much faster than digital computers.

A machine designed to model how the brain performs a task, often implemented with electronic components or software.

A massively parallel distributed processor made up of simple processing units that has a natural propensity for storing experiential knowledge and making it available for use.

Interneuron connection strengths used to store the knowledge acquired by the network.

The procedure used to modify the synaptic weights of a network in an orderly fashion to attain a desired design objective.

The neural network’s production of reasonable outputs for inputs not encountered during training (learning).

A property of neural networks where an interconnection of nonlinear neurons creates a nonlinear network, important for processing inherently nonlinear signals.

Modifying synaptic weights using labeled training examples to minimize the difference between desired and actual responses, creating an input-output mapping.

The built-in capability of neural networks to adjust their synaptic weights in response to changes in the surrounding environment.

The challenge of ensuring an adaptive system remains stable and doesn't overreact to spurious disturbances.

A design feature allowing a neural network to provide confidence levels about its decisions, useful for rejecting ambiguous patterns.

A property where knowledge is represented by the structure and activation state of a network, allowing it to naturally process contextual information.

The potential of a neural network to maintain performance even when neurons or connections are damaged, due to the distributed nature of information storage.

The suitability of neural networks for implementation using very-large-scale-integrated (VLSI) technology due to their massively parallel nature.

A set of synapses organized into patterns of connectivity to produce a functional operation of interest; analogous to a silicon chip.

Maps organized to respond to incoming sensory information, often arranged in sheets such that stimuli from corresponding points in space align.

An information-processing unit that is fundamental to the operation of a neural network.

A set of connecting links, each characterized by a weight or strength, that multiplies the input signal.

A function that limits the amplitude range of the output signal of a neuron to some finite value.

An externally applied input that increases or lowers the net input of the activation function, affecting the neuron's output.

An activation function that outputs one of two values (e.g. 0 or 1) based on whether the input is above or below a threshold.

A type of activation usually used in the construction of neural networks that exhibits a balance between linear and nonlinear behavior.

A neural model where the decision for a neuron to fire is probabilistic, based on the induced local field and a pseudotemperature parameter.

Networks of directed links interconnected at nodes, used to portray the flow of signals in a neural network.

Links governed by a linear input-output relation where the node signal is multiplied by a synaptic weight.

Links governed by a nonlinear input-output relation, typically represented by a nonlinear activation function

A directed graph describing the layout of a neural network, focusing on signal flow from neuron to neuron and omitting details inside individual neuron and

When the output of an element influences the input to that element creating closed paths for signal transmission.

A system where the output is an infinite weighted summation of present and past samples of the input signal, controlled by a weight 'w'.

Approximating a feedback system with a feedforward system, achieved by truncating the series representation; only practical when the feedback system is stable.

A neural network architecture where neurons are organized in layers, with an input layer projecting directly onto an output layer, a feedforward-only network.

A feedforward neural network with one or more hidden layers between the input and output layers, enabling the network to extract higher-order statistics from its input.

A neural network where every node in each layer is connected to every other node in the adjacent forward layer.

A neural network with at least one feedback loop, where the output of a neuron can influence its own input or the inputs of other neurons.

Stored information or models used to interpret, predict, and appropriately respond to the outside world.

A set of input-output pairs used for training a neural network, consisting of an input signal and the corresponding desired response.

Successful performance of a trained network on test patterns not seen during training, demonstrating its ability to apply learned knowledge to new situations.

Patterns drawn from similar classes should produce similar representations inside the network, leading to classification in the same class.

Items to be categorized as separate classes should be given distinctly different representations within the neural network.

If a certain feature is significant, a large number of artificial neurons should be allocated to the representation of that item in the neural network.

If prior information and invariances are available, they should be incorporated in the design of a neural network to ease learning and simplify design.