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Biol Cybern (2006) 95:185192 DOI 10.1007/s00422-006-0076-6
 

Summary: Biol Cybern (2006) 95:185192
DOI 10.1007/s00422-006-0076-6
ORIGINAL PAPER
An algorithmic method for reducing conductance-based neuron models
Michael E. Sorensen Stephen P. DeWeerth
Received: 21 November 2005 / Accepted: 25 April 2006 / Published online: 21 June 2006
Springer-Verlag 2006
Abstract Although conductance-based neural models
provide a realistic depiction of neuronal activity, their
complexity often limits effective implementation and
analysis. Neuronal model reduction methods provide a
means to reduce model complexity while retaining the
original model's realism and relevance. Such methods,
however, typically include ad hoc components that re-
quire that the modeler already be intimately familiar
with the dynamics of the original model. We present
an automated, algorithmic method for reducing conduc-
tance-based neuron models using the method of equiva-
lent potentials (Kelper et al., Biol Cybern 66(5):
381387, 1992) Our results demonstrate that this algo-

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra

 

Collections: Computer Technologies and Information Sciences