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Computational Modeling Methods for Neuroscientists$
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Erik De Schutter

Print publication date: 2009

Print ISBN-13: 9780262013277

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262013277.001.0001

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Modeling Point Neurons: From Hodgkin-Huxley to Integrate-and-Fire

Modeling Point Neurons: From Hodgkin-Huxley to Integrate-and-Fire

(p.161) 7 Modeling Point Neurons: From Hodgkin-Huxley to Integrate-and-Fire
Computational Modeling Methods for Neuroscientists

Nicolas Brunel

The MIT Press

This chapter explores the point neurons, i.e., neurons without shape that form the basis of many network models. It discusses why and in which conditions simplified spiking neurons are useful, and also reviews the prototypical simplified spiking neuron model, the leaky integrate-and-fire (LIF) neuron. The chapter then reports several models that are intermediate between Hodgkin-Huxley (HH)-type neurons and LIF neurons, and argues that an exponential nonlinearity appears the best way to capture the spike generation of HH-type models. It shows that simplified models which are in-between the LIF neuron and HH-type models are very helpful in understanding the wide spectrum of dynamic properties observed in real neurons.

Keywords:   point neurons, integrate and fire, exponential nonlinearity, spiking neurons, HH-type models

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