The shaping of the coherence function of resonate-and-fire neuron models
-
1
Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience Berlin (BCCN), Germany
-
2
Humboldt-Universität zu Berlin, Institute for Theoretical Biology, Germany
-
3
Humboldt-Universität zu Berlin, Institute for Physics, Germany
It is known that integrate-and-fire neurons act as low-pass filters on information [1], i.e. they preferentially encode information at low frequencies. However, many neurons cannot be described by such integrators, even qualitatively. Experiments show that in several areas of the mammalian brain single neurons display a resonance property in their subthreshold voltage dynamics, see for example [2,3]. These neurons are more appropriately described by so called resonate-and-fire neurons [4]. Here, we study the information transmission of a current-based resonate-and-fire neuron model [5] by means of the spectral coherence function. We use a stochastic input current (the Ornstein-Uhlenbeck process from statistical physics) to model a complex dynamical stimulus. We show by numerical simulations that resonate-and-fire neurons encode time-dependent stimuli preferentially at moderate frequencies, including their resonance frequency, i.e. the coherence function of this model shows a clear maximum as a function of frequency. This is in marked contrast to the low-pass coherence that is found for the pure subthreshold dynamics (in the absence of spiking) in spite of resonant filter properties. We discuss dynamical mechanisms that lead to the band-pass filtering of information in the spiking resonate-and-fire model.
Acknowledgements
This work was supported by grants from the BMBF (01GQ0901,01GQ1001A) and Deutsche Forschungsgemeinschaft (SFB 618).
References
[1] R.D.Vilela & B. Lindner,
A comparative study of different integrate fire neurons: spontaneous activity, dynamical response, and stimulus-induced correlation.
Phys. Rev. E 80 (2009)
[2] B. Hutcheon and Y. Yarom 2000,
Resonance, oscillation and the intrinsic frequency preferences of neurons.
TINS 23 (2000): 216-222
[3] I. Erchova, G. Kreck, U. Heinemann and A.V.M. Herz
Dynamics of rat entorhinal cortex layer II/III cells: characteristics of membrane potential resonance at rest predict oscillation properties near threshold,
Journal of Physiology 560 (2004): 89-110
[4] E. M. Izhikevich,
Resonate-and-fire neurons,
Neural Networks 14 (2001): 883-894
[5] T.A. Engel, L. Schimansky-Geier, A.V.M. Herz, S. Schreiber, I. Erchova
Subthreshold membrane-potential resonances shape spike-train patterns in the entorhinal cortex
J. Neurophysiology 100 (2007): 1576-1589
Keywords:
coherence,
frequency selectivity,
Information Theory,
resonate-and-fire
Conference:
Bernstein Conference 2012, Munich, Germany, 12 Sep - 14 Sep, 2012.
Presentation Type:
Poster
Topic:
Neural encoding and decoding
Citation:
Blankenburg
S,
Lindner
B and
Schreiber
S
(2012). The shaping of the coherence function of resonate-and-fire neuron models.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference 2012.
doi: 10.3389/conf.fncom.2012.55.00185
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
11 May 2012;
Published Online:
12 Sep 2012.
*
Correspondence:
Mr. Sven Blankenburg, Humboldt-Universität zu Berlin, Bernstein Center for Computational Neuroscience Berlin (BCCN), Berlin, 10115, Germany, sven.blankenburg@physik.hu-berlin.de