A learning algorithm for a novel neural network architecture motivated by integrate-and-fire neuron model

Title:

A learning algorithm for a novel neural network architecture motivated by integrate-and-fire neuron model

Creator:

Mishra, Deepak, Yadav, Abhishek, and Kalra, Prem K.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:d5397342-b4d2-4b5c-95c4-12f5aa5e9d8d
uuid:d5397342-b4d2-4b5c-95c4-12f5aa5e9d8d

Subject:

Integrate-And-Fire Neuron Model, function approximation, classification, artificial neural network, and steepest descent method

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, a learning algorithm for a novel neural network architecture motivated by Integrate-and-Fire Neuron Model (IFN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that inclusion of a few more biological phenomenon in the formulation of artificial neural networks make them more prevailing. Several benchmark and real-life problems of classification and function-approximation are illustrated.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2006 Volume:16 | Number:6

Harvested from:

CDK

Metadata only:

false