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Stimulus–response model

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The stimulus–response model is a characterization of a statistical unit (such as a neuron) as a black box model, predicting a quantitative response to a quantitative stimulus, for example one administered by a researcher. [1]

9 relations: Black box, Expected value, Linear model, Linear regression, Neuron, Risk assessment, Statistical theory, Statistical unit, Stimulation.

Black box

In science, computing, and engineering, a black box is a device, system or object which can be viewed in terms of its inputs and outputs (or transfer characteristics), without any knowledge of its internal workings.

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Expected value

In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.

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Linear model

In statistics, the term linear model is used in different ways according to the context.

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Linear regression

In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).

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A neuron, also known as a neurone (British spelling) and nerve cell, is an electrically excitable cell that receives, processes, and transmits information through electrical and chemical signals.

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Risk assessment

Risk assessment is the determination of quantitative or qualitative estimate of risk related to a well-defined situation and a recognized threat (also called hazard).

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Statistical theory

The theory of statistics provides a basis for the whole range of techniques, in both study design and data analysis, that are used within applications of statistics.

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Statistical unit

A unit in a statistical analysis is one member of a set of entities being studied.

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Stimulation is the encouragement of development or the cause of activity generally.

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Stimulus Response Model, Stimulus-response, Stimulus-response model, Stimulus–response.


[1] https://en.wikipedia.org/wiki/Stimulus–response_model

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