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Sometimes a firing rate is considered instead of a probability.
A firing rate unit receives constant input (I{(t)} = 1).
A firing rate unit receives a variable synaptic input (I{(t)}).
The above results (valid for slow synapses) can also be obtained using a firing rate approach, as described in [177].
Indeed, given a phase PDF and a firing rate, there are many spike trains satisfying these two conditions.
So, given (nu^ > frac{eta^{2}}{2}), there exists exactly one (g^) at which a firing rate variance of (nu^) is achieved.
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Hertz and Prügel-Bennett [17] implemented a firing rate-based Hebbian plasticity of synapses.
Further, they showed that the amount of ATP increased in a firing rate-dependent manner to stimulation with saccharin, quinine or glutamate.
The present paper uses a firing-rate model to study mechanisms that trigger and stop transitions between tonic and phasic population firing.
We have shown that the formalism introduced in this article can be effectively used to calculate the functional connectivity of neurons within a firing-rate network model.
In this article, we therefore concentrate on computing the correlations among neurons in a firing-rate network, although we also discuss how to compute, with the same formalism, also other measures of functional connectivity (Sect. 5).
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