How to Model Spike-Frequency Adaptation in Integrate-and-Fire Neurons
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1
Ludwig-Maximilians University Munich, Germany
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2
University of Ottawa, Department of Cellular and Molecular Medicine, Canada
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3
University of Ottawa, Department of Physics and Biocenter Oulu,, Canada
Spike-frequency adaptation is a prominent aspect of neuronal dynamics that shapes a neuron's signal processing properties on timescales ranging from about 10 ms to more than 1 s. For integrate-and-fire model neurons spike-frequency adaptation is incorporated either as an adaptation current or as a dynamic firing threshold. We here show that a dynamic threshold has a divisive effect on the f-I curve (firing rate versus input current) whereas an adaptation current shifts the f-I curve to higher inputs without affecting its slope. As a consequence, an adaptation current acts essentially linearly, resulting in a well defined high-pass filter component of the neuron's transfer function. In contrast, with a dynamic threshold the resulting transfer function strongly depends on the input range due to the nonlinear behavior introduced by the multiplicative effect on the f-I curves. Simulations of conductance-based models with various types of adaptation currents, such as M-type, AHP-type, and sodium activated potassium currents, don't show the divisive effects of a dynamic threshold, but are in agreement with the properties of integrate-and-fire neurons with adaptation current. This suggests that one should rather use (simplified) adaptation currents than a dynamic threshold for modelling spike-frequency adaptation with integrate-and-fire model neurons.
Conference:
Bernstein Symposium 2008, Munich, Germany, 8 Oct - 10 Oct, 2008.
Presentation Type:
Poster Presentation
Topic:
All Abstracts
Citation:
Benda
J,
Maler
L and
Longtin
A
(2008). How to Model Spike-Frequency Adaptation in Integrate-and-Fire Neurons.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Symposium 2008.
doi: 10.3389/conf.neuro.10.2008.01.029
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Received:
13 Nov 2008;
Published Online:
13 Nov 2008.
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Correspondence:
Jan Benda, Ludwig-Maximilians University Munich, Munich, Germany, j.benda@biologie.hu-berlin.de