http://www.cnr.it/ontology/cnr/individuo/prodotto/ID275214
Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models (Articolo in rivista)
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- Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models (Articolo in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1038/srep02934 (literal)
- Alternative label
Marasco A, Limongiello A, Migliore M (2013)
Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models
in Scientific reports (Nature Publishing Group); Nature Publishing Group, London (Regno Unito)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Marasco A, Limongiello A, Migliore M (literal)
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- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.nature.com/srep/2013/131014/srep02934/full/srep02934.html (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
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- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Univ Naples Federico II, Dept Math & Applicat R Caccioppoli, Naples, Italy; CNR, Inst Biophys, Palermo, Italy (literal)
- Titolo
- Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models (literal)
- Abstract
- The cellular mechanisms underlying higher brain functions/dysfunctions are extremely difficult to investigate experimentally, and detailed neuron models have proven to be a very useful tool to help these kind of investigations. However, realistic neuronal networks of sizes appropriate to study brain functions present the major problem of requiring a prohibitively high computational resources. Here, building on our previous work, we present a general reduction method based on Strahler's analysis of neuron morphologies. We show that, without any fitting or tuning procedures, it is possible to map any morphologically and biophysically accurate neuron model into an equivalent reduced version. Using this method for Purkinje cells, we demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated synaptic inputs (literal)
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