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conference paper not in proceedings

Complexity and performance in simple neuron models

Mensi, Skander  
•
Naud, Richard  
•
Becker, Thomas K
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2010
2nd INCF Congress of Neuroinformatics

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.

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Type
conference paper not in proceedings
Author(s)
Mensi, Skander  
Naud, Richard  
Becker, Thomas K
Gerstner, Wulfram  
Date Issued

2010

URL

URL

http://www.incf.org/
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

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LCN  
Event nameEvent placeEvent date
2nd INCF Congress of Neuroinformatics

Pilsen, Czech Republic

September 06 - 08, 2009

Available on Infoscience
November 11, 2009
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/44186
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