scholarly journals Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron

2003 ◽  
Vol 15 (10) ◽  
pp. 2281-2306 ◽  
Author(s):  
Nicolas Brunel ◽  
Peter E. Latham

We calculate the firing rate of the quadratic integrate-and-fire neuron in response to a colored noise input current. Such an input current is a good approximation to the noise due to the random bombardment of spikes, with the correlation time of the noise corresponding to the decay time of the synapses. The key parameter that determines the firing rate is the ratio of the correlation time of the colored noise, τs, to the neuronal time constant, τm. We calculate the firing rate exactly in two limits: when the ratio, τs/τm, goes to zero (white noise) and when it goes to infinity. The correction to the short correlation time limit is O(τs/τm), which is qualitatively different from that of the leaky integrate-and-fire neuron, where the correction is O(√τs/τm). The difference is due to the different boundary conditions of the probability density function of the membrane potential of the neuron at firing threshold. The correction to the long correlation time limit is O(τm/τs). By combining the short and long correlation time limits, we derive an expression that provides a good approximation to the firing rate over the whole range of τs/τm in the suprathreshold regime—that is, in a regime in which the average current is sufficient to make the cell fire. In the subthreshold regime, the expression breaks down somewhat when τs becomes large compared to τm.

1998 ◽  
Vol 10 (8) ◽  
pp. 1987-2017 ◽  
Author(s):  
Richard Kempter ◽  
Wulfram Gerstner ◽  
J. Leo van Hemmen ◽  
Hermann Wagner

How does a neuron vary its mean output firing rate if the input changes from random to oscillatory coherent but noisy activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence-detection properties of an integrate-and-fire neuron. We derive an expression indicating how coincidence detection depends on neuronal parameters. Specifically, we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thus can convert a temporal code into a rate code.


2002 ◽  
Vol 14 (9) ◽  
pp. 2111-2155 ◽  
Author(s):  
Emilio Salinas ◽  
Terrence J. Sejnowski

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron's response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that represents the total input current. Analytic expressions were obtained for the average firing rate and coefficient of variation (a measure of spike-train variability) as functions of the mean, variance, and correlation time of the stochastic input. The results of computer simulations were in excellent agreement with these expressions. In these models, an increase in correlation time in general produces an increase in both the average firing rate and the variability of the output spike trains. However, the magnitude of the changes depends differentially on the relative values of the input mean and variance: the increase in firing rate is higher when the variance is large relative to the mean, whereas the increase in variability is higher when the variance is relatively small. In addition, the firing rate always tends to a finite limit value as the correlation time increases toward infinity, whereas the coefficient of variation typically diverges. These results suggest that temporal correlations may play a major role in determining the variability as well as the intensity of neuronal spike trains.


2019 ◽  
Vol 118 (1) ◽  
pp. 42-47
Author(s):  
KwangSeok Han

Background/Objectives: This study investigated differences in the attitude of users according to type of scarcity message and price discount conditions to compose T-commerce sales messages and search for effective strategic plans. Methods/Statistical analysis: This study empirically verifies the difference in promotion attitude and purchase intention between the type of T-Commerce scarcity message (quantity limit message / time limit message) and the price discount policy (price discount / non-discount) message. For this purpose, 2 (scarcity type: limited quantity, limited time) X 2 (with or without price discount: price discount, no price discount) factor design between subjects was used.


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