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To take just one example, a standard model for neuron response assumes that activation is a nonlinear function of a weighted sum of the inputs.
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We modify this model for neurons that have broader action potentials or those for which the burst envelope instead of individual spikes are modeled.
Several kinds of well-established cell models for neuron cell exocytosis study have been widely used for this kind of study.
C. elegans is a useful model organism for neuron regeneration because it is optically transparent, and its entire neural network is known.
There is another useful database, DOQCS, which serves as the in silico quantitative model repository for neuron and other signaling pathways.
JAMES DICARLO: Point neurons are not AUDIENCE: Linear, non-linear models for neurons, they aren't perfect models of and the feedback stuff.
In general, mathematical models for neurons are of this type.
We consider two recent models for neurons in the pre-BötC.
We have considered how certain constrained, positive inputs should be timed to yield maximal numbers of spikes in the LIF and theta models for neurons.
Nonlinear Noisy Leaky Integrate and Fire (NNLIF) models for neurons networks can be written as Fokker-Planck-Kolmogorov equations on the probability density of neurons, the main parameters in the model being the connectivity of the network and the noise.
Now that we have introduced some oscillator models for neurons and neural populations it is appropriate to consider the set of tools for analysing their behaviour at the network level.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com