Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

Eoin Lynch, Conor J Houghton

Research output: Contribution to journalArticle (Academic Journal)peer-review

12 Citations (Scopus)
250 Downloads (Pure)


Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.
Original languageEnglish
Article number10
Number of pages15
JournalFrontiers in Neuroinformatics
Publication statusPublished - 20 Apr 2015


  • cell
  • neuron
  • parameter estimation
  • simulation

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