Complexity and performance in simple neuron models
-
1
Brain Mind Institute,EPFL, Switzerland
The ability of simple mathematical models to predict the activity of single neurons is important for computational neuroscience. In neurons, stimulated by a time-dependent current or conductance, we want to predict precisely the timing of spikes and the sub-threshold voltage. During the last years several models have been tested on this type of data but never compared with the same protocol. One of the major outcome is that, from a certain degree of complexity, all are very efficient and gave statistically indistinguishable results.
We studied a class of integrate-and-fire models (IF), with each member of the class implementing a selection of possible improvements: exponential voltage non-linearity1, spike-triggered adaptation current2, spike-triggered change in conductance, moving threshold3, sub-threshold voltage-dependent currents4. Each refinement adds a new term to the equations of the IF model. This IF family is extendable and adaptable to different neuron types and is able to deal with complex neural activities (i.e. adaptation, facilitation, bursting, relative refractoriness, ...). To systematically explore the effects of a given term of the model a new fitting procedure based on linear regression of voltage change5 is used. This method is fast, robust and allows the extraction of all the models parameters from a few seconds recordings of fluctuating injected current and membrane potential with hundreds spikes without any prior knowledge. To investigate the effect of our modifications, we used as training data three different Hodgkin-Huxley-like models, and two experimental recordings of fast spiking and regular spiking cells and then evaluate each IF model on a given test set.
We observe that it is possible to fit a model that can reproduce the activity of neurons with high reliability (i.e. almost 100 % of the spike time and less than 1 mV of sub-threshold voltage difference). Using this framework one can classify IF models in terms of complexity and performance and evaluate the importance of each term for different stimulation paradigms.
References
1. Fourcaud-Trocme N., Hansel D., van Vreeswijk C., and Brunel N. (2003), How spike generation mechanisms determine the neuronal response to fluctuating inputs, J. Neuroscience 23:11628-11640.
2. Brette R. and Gerstner W. (2005), Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity, J. Neurophysiol. 94: 3637 - 3642.
3. Badel L., Lefort S., Brette R., Petersen C., Gerstner W. and Richardson M.J.E. (2008), Dynamic I-V Curves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces, J Neurophysiol 99: 656 - 666.
4. Richardson M.J.E., Brunel N., and Hakim V (2003), From Subthreshold to Firing-Rate Resonance. Journal of Neurophysiology 89:2538-2554.
5. Paninski, L., Pillow, J. & Simoncelli, E. (2004). Comparing integrate-and-fire-like models estimated using intra- and extra-cellular data. Neurocomputing 65: 379-385.
Conference:
Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009.
Presentation Type:
Poster Presentation
Topic:
Computational neuroscience
Citation:
Mensi
S,
Naud
R,
Becker
TK and
Gerstner
W
(2019). Complexity and performance in simple neuron models.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2009.
doi: 10.3389/conf.neuro.11.2009.08.087
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:
22 May 2009;
Published Online:
09 May 2019.
*
Correspondence:
Skander Mensi, Brain Mind Institute,EPFL, Lausanne, Switzerland, skander.mensi@epfl.ch