Event Abstract Back to Event Complexity and performance in simple neuron models Skander Mensi1*, Richard Naud1, Michael Avermann2, Carl Petersen2 and Wulfram Gerstner1 1 EPFL-LCN, Switzerland 2 EPFL, Brain-Mind Institute , Switzerland The ability of simple mathematical models to predict the activity of single neurons is important for computational neuroscience. For 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. One of the major outcome is that, from a certain degree of complexity, all are very efficient. However, models have never been systematically compared with the same protocol. We study a class of integrate-and-fire models (IF), with each member of the class implementing a selection of possible improvements: exponential voltage non-linearity [1], spike-triggered adaptation current [2], spike-triggered change in conductance, moving threshold [3], sub-threshold voltage-dependent currents [4]. 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 change [5] is used in combination with a novel method to extract dynamic threshold and spike-triggered adaptation. This method is fast, robust and allows the extraction of all the models parameters from a few seconds of patch-clamp recordings during injection of a fluctuating current. To investigate the effect of our approach, we applied it to artificial data from Hodgkin-Huxley-like models, as well as experimental data from fast spiking and pyramidal cells. We observe that it is possible to tune the model so that it 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) on new data that was not used for parameter optimization. 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.