liquid crystal display (LCD)

Optoelectronic device used in displays for watches, calculators, notebook computers, and other electronic devices. Current passed through specific portions of the liquid crystal solution causes the crystals to align, blocking the passage of light. Doing so in a controlled and organized manner produces visual images on the display screen. The advantage of LCDs is that they are much lighter and consume less power than other display technologies (e.g., cathode-ray tubes). These characteristics make them an ideal choice for flat-panel displays, as in portable laptop and notebook computers.

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Substance that flows like a liquid but maintains some of the ordered structure characteristic of a crystal. Some organic substances do not melt directly when heated but instead turn from a crystalline solid to a liquid crystalline state. When heated further, a true liquid is formed. Liquid crystals have unique properties. The structures are easily affected by changes in mechanical stress, electromagnetic fields, temperature, and chemical environment. Seealso liquid crystal display.

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One of the three principal states of matter, intermediate between a gas and a solid. A liquid has neither the orderliness of a solid nor the randomness of a gas. Liquids have the ability to flow under the action of very small shear stresses. Liquids in contact with their own vapour or air have a surface tension that causes the interface to assume the configuration of minimum area (i.e., spherical). Surfaces between liquids and solids have interfacial tensions that determine whether the liquid will wet the other material. With the exception of liquid metals, molten salts, and solutions of salts, the electrical conductivities of liquids are small.

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A liquid state machine (LSM) is a computational construct, like a neural network. An LSM consists of a large collection of units (called nodes, or neurons). Each node receives time varying input from external sources (the inputs) as well as from other nodes. Nodes are randomly connected to each other. The recurrent nature of the connections turns the time varying input into a spatio-temporal pattern of activations in the network nodes. The spatio-temporal patterns of activation are read out by linear discriminant units.

The soup of recurrently connected nodes will end up computing a large variety of nonlinear functions on the input. Given a large enough variety of such nonlinear functions, it is theoretically possible to obtain linear combinations (using the read out units) to perform whatever mathematical operation is needed to perform a certain task, such as speech recognition or computer vision.

The word liquid in the name comes from the analogy drawn to dropping a stone into a still body of water or other liquid. The falling stone will generate ripples in the liquid. The input (motion of the falling stone) has been converted into a spatio-temporal pattern of liquid displacement (ripples).

LSMs have been put forward as a way to explain the operation of brains. LSMs are argued to be an improvement over the theory of artificial neural networks because:

1. Circuits are not hand coded to perform a specific task.
2. Continuous time inputs are handled "naturally".
3. Computations on various time scales can be done using the same network.
4. The same network can perform multiple computations.

Criticisms of LSMs as used in computational neuroscience are that

1. LSMs don't actually explain how the brain functions. At best they can replicate some parts of brain functionality.
2. There is no guaranteed way to dissect a working network and figure out how or what computations are being performed.
3. Very little control over the process.
4. Inefficient from an implementation point of view because they require lots of computations, compared to custom designed circuits, or even neural networks.

Universal function approximation

If a reservoir has fading memory and input separability, with help of a powerful readout, it can be proven the liquid state machine is a universal function approximator using Stone-Weierstrass theorem.

See also

• Echo state network: similar concept in recurrent neural network.

References