Response to a periodic stimulus in a perfect integrate-and-fire neuron model driven by colored noise

2016 ◽  
Vol 94 (6) ◽  
Author(s):  
Romi Mankin ◽  
Astrid Rekker
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.


2015 ◽  
Vol 91 (2) ◽  
Author(s):  
Finn Müller-Hansen ◽  
Felix Droste ◽  
Benjamin Lindner

2019 ◽  
Vol 16 (9) ◽  
pp. 3897-3905
Author(s):  
Pankaj Kumar Kandpal ◽  
Ashish Mehta

In the present article, two-dimensional “Spiking Neuron Model” is being compared with the fourdimensional “Integrate-and-fire Neuron Model” (IFN) using error correction back propagation learning algorithm (error correction learning). A comparative study has been done on the basis of several parameters like iteration, execution time, miss-classification rate, number of iterations etc. The authors choose the five-bit parity problem and Iris classification problem for the present study. Results of simulation express that both the models are capable to perform classification task. But single spiking neuron model having two-dimensional phenomena is less complex than Integrate-fire-neuron, produces better results. On the contrary, the classification performance of single ingrate-and-fire neuron model is not very poor but due to complex four-dimensional architecture, miss-classification rate is higher than single spiking neuron model, it means Integrate-and-fire neuron model is less capable than spiking neuron model to solve classification problems.


2009 ◽  
Vol 21 (2) ◽  
pp. 353-359 ◽  
Author(s):  
Hans E. Plesser ◽  
Markus Diesmann

Lovelace and Cios ( 2008 ) recently proposed a very simple spiking neuron (VSSN) model for simulations of large neuronal networks as an efficient replacement for the integrate-and-fire neuron model. We argue that the VSSN model falls behind key advances in neuronal network modeling over the past 20 years, in particular, techniques that permit simulators to compute the state of the neuron without repeated summation over the history of input spikes and to integrate the subthreshold dynamics exactly. State-of-the-art solvers for networks of integrate-and-fire model neurons are substantially more efficient than the VSSN simulator and allow routine simulations of networks of some 105 neurons and 109 connections on moderate computer clusters.


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