There are lots of mechanistic models of excitable cells, some of which include cable properties, but most neuroscientists would not say that any of them is an HH model unless it reuses the originally proposed HH model's equations (updated to the convention that the extracellular potential is 0).
We examine the problem of parameter estimation in mathematical models of excitable cell cardiac electrical activity using the well-known Beeler Reuter (1977) ionic equations for the ventricular action potential.
Our goal here is to illustrate, by exploiting specific models of excitable membrane, some of the concepts and techniques which can be used to understand, predict, and interpret the excitable and oscillatory behaviours that are commonly observed in single cell electrophysiological recordings.
Such oscillations have been measured in neurons and agree with simulations using models of excitable cells [4], [5].
To extend these results, the activity of the PTP was assessed in a model of excitable cells with high metabolic demand expressing mostly HK II, i.e. freshly isolated adult rat cardiac myocytes [28].
In the example, we consider the classical model of excitable membrane by Morris and Lecar (1981).
Early attempts at computational modelling of excitable tissues date back to the 1940s, targeting neural networks, which represents work that subsequently gave rise to both biological and artificial intelligence/machine learning tools.
A simple mathematical model of an excitable fixed bed is constructed and its rich dynamics is analyzed in order to develop a methodology for motion identification and classification.
A two-dimensional cellular automaton (CA) model of an excitable medium is coupled with an array of micro-actuators in the form of abstract air-jets.
We investigate firing threshold manifolds in a mathematical model of an excitable neuron.
