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The adaptive exponential integrate-and-fire model inherits the experimentally derived voltage nonlinearity [4] of the exponential integrate-and-fire model. But going beyond this model, it can also account for a variety of neuronal firing patterns in response to constant stimulation, including adaptation, bursting and initial bursting.
The theta model, or Ermentrout–Kopell canonical Type I model, is mathematically equivalent to the quadratic integrate-and-fire model which in turn is an approximation to the exponential integrate-and-fire model and the Hodgkin-Huxley model. It is called a canonical model because it is one of the generic models for constant input close to the ...
Each neuron model has its appropriate solver and many models have unit tests. If possible, exact integration [3] is used. By default, spikes fall onto the grid, defined by the simulation time-step. Some models support spike-exchange in continuous time. [4]
The biologically inspired Hodgkin–Huxley model of a spiking neuron was proposed in 1952. This model describes how action potentials are initiated and propagated. . Communication between neurons, which requires the exchange of chemical neurotransmitters in the synaptic gap, is described in various models, such as the integrate-and-fire model, FitzHugh–Nagumo model (1961–1962), and ...
One of his main contributions was to propose the integrate-and-fire model of the neuron in a seminal article published in 1907. [2] Today, this model of the neuron is still one of the most popular models in computational neuroscience for both cellular and neural networks studies, as well as in mathematical neuroscience because of its simplicity.
The quadratic integrate and fire (QIF) model is a biological neuron model that describes action potentials in neurons. In contrast to physiologically accurate but computationally expensive neuron models like the Hodgkin–Huxley model, the QIF model seeks only to produce action potential-like patterns by ignoring the dynamics of transmembrane currents and ion channels.
In neuroscience, a frequency-current curve (fI or F-I curve) is the function that relates the net synaptic current (I) flowing into a neuron to its firing rate (F) [1] [2] Because the f-I curve only specifies the firing rate rather than exact spike times, it is a concept suited to the rate coding rather than temporal coding model of neuronal computation.
D. Lawrence Kincaid (born 1945) is an American communication researcher who originated the convergence theory of communication. He was a senior advisor for the Research and Evaluation Division of the Center for Communication Programs and an associate scientist in the Faculty of Social and Behavioral Sciences at the Johns Hopkins Bloomberg School of Public Health.