This title appears in the Scientific Report :
2018
Please use the identifier:
http://dx.doi.org/10.1007/s10827-018-0693-9 in citations.
Please use the identifier: http://hdl.handle.net/2128/19866 in citations.
Firing-rate models for neurons with a broad repertoire of spiking behaviors
Firing-rate models for neurons with a broad repertoire of spiking behaviors
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 mul...
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Personal Name(s): | Heiberg, Thomas |
---|---|
Kriener, Birgit / Tetzlaff, Tom / Einevoll, Gaute T. / Plesser, Hans E. (Corresponding author) | |
Contributing Institute: |
Jara-Institut Brain structure-function relationships; INM-10 Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: | Journal of computational neuroscience, 45 (2018) 2, S. 103-132 |
Imprint: |
Dordrecht [u.a.]
Springer Science + Business Media B.V
2018
|
PubMed ID: |
30146661 |
DOI: |
10.1007/s10827-018-0693-9 |
Document Type: |
Journal Article |
Research Program: |
Brain-inspired multiscale computation in neuromorphic hybrid systems Supercomputing and Modelling for the Human Brain Human Brain Project Specific Grant Agreement 1 Human Brain Project Specific Grant Agreement 2 Theory, modelling and simulation |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/19866 in citations.
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520 | |a 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. | ||
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