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This article is part of the supplement: Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

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Predicting neuronal activity with an adaptive exponential integrate-and-fire model

Nicolas Marcille1, Claudia Clopath1*, Rajnish Ranjan2, Shaul Druckmann3, Felix Schuermann2, Henry Markram2 and Wulfram Gerstner1

Author Affiliations

1 LCN, Brain Mind Institute, EPFL, CH-1015 Lausanne, Switzerland

2 LNMC, Brain Mind Institute, EPFL, CH-1015 Lausanne, Switzerland

3 Department of Neurobiology and Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel

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BMC Neuroscience 2007, 8(Suppl 2):P121  doi:10.1186/1471-2202-8-S2-P121

The electronic version of this article is the complete one and can be found online at:

Published:6 July 2007

© 2007 Marcille et al; licensee BioMed Central Ltd.

Poster presentation

An adaptive Exponential Integrate-and-Fire (aEIF) model [1] was used to predict activity of cortical neurons. This model is a leaky Integrate-and-Fire which has in the voltage equation an additional exponential term [2] describing early activation of voltage-gated channels combined with a second variable introduced in the model to allow for subthreshold and spike frequency adaptation [3].

Previously, we used the aEIF model to predict the membrane potential of pyramidal neurons under random current injection [4]. Moreover, similarly to the Izhikevich model [3], we know that the model can mimic more complicated firing patterns, that is, the model can reproduce spike trains of a detailed conductance-based model under standard electrophysiological paradigms [1].

Here, we reproduce several firing patterns of mainly inter-neurons from the EPFL microcircuit database [5]. The aEIF model was used to reproduce the firing pattern of the different electric classes of neurons under standard electrophysiological input regime. We studied nine classes among which Delayed Initiation Spiking, Burst Spiking, Fast Adapting or Non-Adapting Spiking [6] and compared simulation of the aEIF model (with 9 parameters) to a Hodgkin-and-Huxley model with 6 different ion channels.

Moreover, we wondered whether the model can be fitted directly to experimental data. We successful fitted the aEIF model to recordings of a Layer-II-III cells with different firing properties.

In summary, we found different areas of the parameter space corresponding to these specific classes. That is, the aEIF model includes an additional mechanism that can be tuned to model spike-frequency adaptation as well as burst activity. The exponential term allows one to model specific behaviors such as delayed spike initiation and offers flexibility at the level of the threshold mechanism. At the moment a large part of the tuning is done manually. However, once our automatic parameter fitting procedure is in place, we expect that clustering in parameter space could contribute to an automatic neuron classification.


This work has been supported by the European grant FACETS.


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