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

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.

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PERIODICALLY FORCED PIECEWISE-LINEAR ADAPTIVE EXPONENTIAL INTEGRATE-AND-FIRE NEURON

International Journal of Bifurcation and Chaos ◽

10.1142/s021812741350171x ◽

2013 ◽

Vol 23 (10) ◽

pp. 1350171 ◽

Cited By ~ 1

Author(s):

LIEJUNE SHIAU ◽

CARLO R. LAING

Keyword(s):

Parameter Space ◽

Action Potentials ◽

Neuron Model ◽

Piecewise Linear ◽

Model Neuron ◽

Spiking Neuron ◽

Integrate And Fire ◽

Spiking Neuron Model ◽

Functional Features ◽

Integrate And Fire Neuron

Although variability is a ubiquitous characteristic of the nervous system, under appropriate conditions neurons can generate precisely timed action potentials. Thus considerable attention has been given to the study of a neuron's output in relation to its stimulus. In this study, we consider an increasingly popular spiking neuron model, the adaptive exponential integrate-and-fire neuron. For analytical tractability, we consider its piecewise-linear variant in order to understand the responses of such neurons to periodic stimuli. There exist regions in parameter space in which the neuron is mode locked to the periodic stimulus, and instabilities of the mode locked states lead to an Arnol'd tongue structure in parameter space. We analyze mode locked solutions and examine the bifurcations that define the boundaries of the tongue structures. The theoretical analysis is in excellent agreement with numerical simulations, and this study can be used to further understand the functional features related to responses of such a model neuron to biologically realistic inputs.

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Identification of Linear and Nonlinear Sensory Processing Circuits from Spiking Neuron Data

Neural Computation ◽

10.1162/neco_a_01051 ◽

2018 ◽

Vol 30 (3) ◽

pp. 670-707 ◽

Cited By ~ 3

Author(s):

Dorian Florescu ◽

Daniel Coca

Keyword(s):

Sensory Processing ◽

Neuron Model ◽

Linear Filter ◽

Spiking Neuron ◽

Neuron Models ◽

Integrate And Fire ◽

Spiking Neuron Model ◽

In Series ◽

Linear And Nonlinear ◽

Model Equations

Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms.

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Simplicity and Efficiency of Integrate-and-Fire Neuron Models

Neural Computation ◽

10.1162/neco.2008.03-08-731 ◽

2009 ◽

Vol 21 (2) ◽

pp. 353-359 ◽

Cited By ~ 12

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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