Related Experiment Video
Updated: Aug 11, 2026

Dynamic Quantitative Sensory Testing to Characterize Central Pain Processing
Published on: February 16, 2017
Nonlinearities in the coding of intensity within the context of a temporal summation model
Abstract:
A model of temporal summation and intensity coding relates the subject's internal percept y(t) to the stimulus input x(t) by the equation y(t) = g(St - oof[x(tau)] h [t, tau, x (tau)]d tau). In words, some transformation f[x (t)] of the stimulus intensity is weighted by a function h and integrated; the result is transformed into the internal percept by a function g. This system postulates a linear integral operator preceded and followed by transformations which may be nonlinear. Based on forward masking of clicks by white noise, we (1) show that the above characterization of the model is appropriate (which involves showing that there is a linear temporal summation stage), and (2) derive certain characteristics of the system's nonlinearities. In particular, the integral of h times f is shown to be a nonlinear function of the input intensity exhibiting more compression than a power function. It is also shown that h must depend upon the intensity of the stimulus.
Related Concept Videos
Muscle Stimulation Frequency
Wave summation
At low firing rates, motor neurons induce individual twitch contractions in muscle fibers. These twitches...
Graded Potential
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...
Integration of Synaptic Events
Nonlinear Pharmacokinetics: Overview
Nonlinearity can arise due to the saturation of plasma protein-binding or...
Nonlinear Pharmacokinetics: Causes of Nonlinearity
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model

