CRPSO-Based Integrate-and-Fire Neuron Model for Time Series Prediction

2010 ◽  
pp. 100-107
Author(s):  
Liang Zhao ◽  
Feng Qian
Keyword(s):  
Time Series ◽  
Neuron Model ◽  
Time Series Prediction ◽  
Integrate And Fire ◽  
Integrate And Fire Neuron

Time-series prediction with single integrate-and-fire neuron

2007 ◽  
Vol 7 (3) ◽  
pp. 739-745 ◽  
Author(s):  
A. Yadav ◽  
D. Mishra ◽  
R.N. Yadav ◽  
S. Ray ◽  
P.K. Kalra
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Integrate And Fire ◽  
Integrate And Fire Neuron

Critical Analysis of Two Dimensional and Four-Dimensional Spiking Neuron Models

2019 ◽  
Vol 16 (9) ◽  
pp. 3897-3905
Author(s):  
Pankaj Kumar Kandpal ◽  
Ashish Mehta
Keyword(s):  
Error Correction ◽  
Neuron Model ◽  
Learning Algorithm ◽  
Two Dimensional ◽  
Spiking Neuron ◽  
Classification Rate ◽  
Integrate And Fire ◽  
Study Results ◽  
Spiking Neuron Model ◽  
Integrate And Fire Neuron

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.

Simplicity and Efficiency of Integrate-and-Fire Neuron Models

2009 ◽  
Vol 21 (2) ◽  
pp. 353-359 ◽  
Author(s):  
Hans E. Plesser ◽  
Markus Diesmann
Keyword(s):  
Neuron Model ◽  
State Of The Art ◽  
Network Modeling ◽  
Spiking Neuron ◽  
Neuron Models ◽  
Computer Clusters ◽  
Integrate And Fire ◽  
The Past ◽  
History Of ◽  
Integrate And Fire Neuron

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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