Learning with Single Quadratic Integrate-and-Fire Neuron

  • Deepak Mishra
  • Abhishek Yadav
  • Prem K. Kalra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, a learning algorithm for a single Quadratic Integrate-and-Fire Neuron (QIFN) is proposed and tested for various applications in which a multilayer perceptron neural network is conventionally used. It is found that a single QIFN is sufficient for the applications that require a number of neurons in different layers of a conventional neural network. Several benchmark and real-life problems of classification and function-approximation have been illustrated.


Temporal Summation Spike Neural Network Multilayer Perceptron Neural Network Phase Reset Curve Spike Neuron Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Deepak Mishra
    • 1
  • Abhishek Yadav
    • 2
  • Prem K. Kalra
    • 1
  1. 1.Department of Electrical EngineeringIIT KanpurIndia
  2. 2.Department of Electrical EngineeringG.B. Pant University of Agri. & TechnologyPant NagarIndia

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