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  • Poster presentation
  • Open Access

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

  • 1,
  • 1Email author,
  • 2,
  • 3,
  • 2,
  • 2 and
  • 1
BMC Neuroscience20078 (Suppl 2) :P121

  • Published:


  • Spike Train
  • Firing Pattern
  • Exponential Term
  • Threshold Mechanism
  • Burst Spike

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.

Authors’ Affiliations

LCN, Brain Mind Institute, EPFL, CH-1015 Lausanne, Switzerland
LNMC, Brain Mind Institute, EPFL, CH-1015 Lausanne, Switzerland
Department of Neurobiology and Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem, Israel


  1. Brette R, Gerstner W: Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol. 2005, 94: 3637-3642. 10.1152/jn.00686.2005.PubMedView ArticleGoogle Scholar
  2. Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N: How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci. 2003, 23: 11628-11640.PubMedGoogle Scholar
  3. Izhikevich E: Which model to use for cortical spiking neurons?. IEEE Trans Neural Netw. 2004, 15: 1063-1070. 10.1109/TNN.2004.832719.PubMedView ArticleGoogle Scholar
  4. Clopath C, Jolivet R, Rauch A, Lüscher H-R, Gestner W: A computational model relating changes in cerebral blood volume to synaptic activity in neurons. Neurocomputing. 2007, 70: 1674-1679. 10.1016/j.neucom.2006.10.047.View ArticleGoogle Scholar
  5. EPFL Neocortical microcircuit database. []
  6. Petilla 2005 Convention. []


© Marcille et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.