Firing-rate models for neurons with a broad repertoire of spiking behaviors

Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute T.; Plesser, Hans E.

doi:10.1007/s10827-018-0693-9

Journal Article

FZJ-2018-05077

Firing-rate models for neurons with a broad repertoire of spiking behaviors

Heiberg, T. ; Kriener, B. ; Tetzlaff, T.FZJ* ; Einevoll, G. T. ; Plesser, H. E. (Corresponding author)FZJ*

2018
Springer Science + Business Media B.V Dordrecht [u.a.]

Journal of computational neuroscience 45(2), 103-132 (2018) [10.1007/s10827-018-0693-9]

This record in other databases:

Please use a persistent id in citations: http://hdl.handle.net/2128/19866 doi:10.1007/s10827-018-0693-9

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.

Classification: