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An effective probabilistic algorithm is given to model the firing rate of the neurons, while the noise sources are coupled.

Another major difference is that, in our model, the firing rate does not explicitly depend on the average reception rate σ as it is the case in the leaky integrate-and-fire population density models ([6, 11]).

In contrast to the QIF model, the firing rate of the EIF is approximately linear for large input (the precise dependence is Im/log(Im)).

As with the QIF model, the firing rate depends only indirectly on input current variables because of the low probability distribution near spike threshold.

In terms of the spiking model the firing rate model trajectory corresponds to the mean of the trajectory of all units.

(B )–(E ) Various activity patterns used to model the firing of presynaptic input neurons.

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In the forest-fire model, the fire pauses when it has to pass through a single B tree.

In this work we use spatial marked point processes to model the fire size of the forest fires observed in Castellón, Spain, during the years 2001 2006.

The fire-structure simulation model was successfully applied to model the fire-thermal-structural behaviors in two localized fire tests on a real-scale steel beam recently conducted at the National Fire Research Laboratory (NFRL) of the National Institute of Standards and Technology NISTT).

p ( s, N ) models the firing rate of neurons submitted to a stimulation of amplitude N and such that the time elapsed since the last discharge is s.

In 'classical' neural field models the firing rate of a neuronal population is assumed to be given by its instantaneous input, which is only valid for tonically spiking neurons.

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