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| Hauptseite > Publikationsdatenbank > Firing-rate models for neurons with a broad repertoire of spiking behaviors > print |
| Hauptseite > Publikationsdatenbank > Firing-rate models for neurons with a broad repertoire of spiking behaviors > print |
| 001 | 851430 | ||
| 005 | 20240313103128.0 | ||
| 024 | 7 | _ | |a 10.1007/s10827-018-0693-9 |2 doi |
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| 037 | _ | _ | |a FZJ-2018-05077 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Heiberg, Thomas |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Firing-rate models for neurons with a broad repertoire of spiking behaviors |
| 260 | _ | _ | |a Dordrecht [u.a.] |c 2018 |b Springer Science + Business Media B.V |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 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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| 700 | 1 | _ | |a Plesser, Hans E. |0 P:(DE-Juel1)169781 |b 4 |e Corresponding author |
| 773 | _ | _ | |a 10.1007/s10827-018-0693-9 |0 PERI:(DE-600)1473055-8 |n 2 |p 103-132 |t Journal of computational neuroscience |v 45 |y 2018 |x 0929-5313 |
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