dc.date.accessioned | 2019-01-24T12:19:35Z | |
dc.date.available | 2019-01-24T12:19:35Z | |
dc.date.created | 2018-08-25T13:28:20Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Heiberg, Thomas Kriener, Birgit Tetzlaff, Tom Einevoll, Gaute Plesser, Hans Ekkehard . Firing-rate models for neurons with a broad repertoire of spiking behaviors. Journal of Computational Neuroscience. 2018, 45(2), 103-132 | |
dc.identifier.uri | http://hdl.handle.net/10852/66309 | |
dc.description.abstract | Capturing the response behavior of spiking neuron models with rate-based models facilitates the investigation of neuronal networks using powerful methods for rate-based network dynamics. To this end, we investigate the responses of two widely used neuron model types, the Izhikevich and augmented multi-adapative threshold (AMAT) models, to a range of spiking inputs ranging from step responses to natural spike data. We find (i) that linear-nonlinear firing rate models fitted to test data can be used to describe the firing-rate responses of AMAT and Izhikevich spiking neuron models in many cases; (ii) that firing-rate responses are generally too complex to be captured by first-order low-pass filters but require bandpass filters instead; (iii) that linear-nonlinear models capture the response of AMAT models better than of Izhikevich models; (iv) that the wide range of response types evoked by current-injection experiments collapses to few response types when neurons are driven by stationary or sinusoidally modulated Poisson input; and (v) that AMAT and Izhikevich models show different responses to spike input despite identical responses to current injections. Together, these findings suggest that rate-based models of network dynamics may capture a wider range of neuronal response properties by incorporating second-order bandpass filters fitted to responses of spiking model neurons. These models may contribute to bringing rate-based network modeling closer to the reality of biological neuronal networks. | |
dc.language | EN | |
dc.publisher | Kluwer Academic Publishers | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Firing-rate models for neurons with a broad repertoire of spiking behaviors | |
dc.type | Journal article | |
dc.creator.author | Heiberg, Thomas | |
dc.creator.author | Kriener, Birgit | |
dc.creator.author | Tetzlaff, Tom | |
dc.creator.author | Einevoll, Gaute | |
dc.creator.author | Plesser, Hans Ekkehard | |
cristin.unitcode | 185,15,4,0 | |
cristin.unitname | Fysisk institutt | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 1604500 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal of Computational Neuroscience&rft.volume=45&rft.spage=103&rft.date=2018 | |
dc.identifier.jtitle | Journal of Computational Neuroscience | |
dc.identifier.volume | 45 | |
dc.identifier.issue | 2 | |
dc.identifier.startpage | 103 | |
dc.identifier.endpage | 132 | |
dc.identifier.doi | http://dx.doi.org/10.1007/s10827-018-0693-9 | |
dc.identifier.urn | URN:NBN:no-69516 | |
dc.type.document | Tidsskriftartikkel | |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0929-5313 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/66309/1/JournofCompNeuroscis10827-018-0693-9.pdf | |
dc.type.version | PublishedVersion | |
dc.relation.project | EC/FP7/269921 | |
dc.relation.project | EC/H2020/720270 | |
dc.relation.project | EC/FP7/604102 | |
dc.relation.project | EC/H2020/785907 | |
dc.relation.project | NOTUR/NORSTORE/NN4661K | |
dc.relation.project | NFR/178892 | |