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Conference Paper

Integrate-and-Fire models with adaptation are good enough: predicting spike times under random current injection

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Rauch,  A
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Jolivet, R., Rauch, A., Lüscher, H.-R., & Gerstner, W. (2006). Integrate-and-Fire models with adaptation are good enough: predicting spike times under random current injection. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in Neural Information Processing Systems 18 (pp. 595-602). Cambridge, MA, USA: MIT Press.


Cite as: https://hdl.handle.net/21.11116/0000-0005-2185-F
Abstract
Integrate-and-Fire-type models are usually criticized because of their simplicity. On the other hand, the Integrate-and-Fire model is the basis of most of the theoretical studies on spiking neuron models. Here, we develop a sequential procedure to quantitatively evaluate an equivalent Integrate-and-Fire-type model based on intracellular recordings of cortical pyramidal neurons. We find that the resulting effective model is sufficient to predict the spike train of the real pyramidal neuron with high accuracy. In in vivo-like regimes, predicted and recorded traces are almost indistinguishable and a significant part of the spikes can be predicted at the correct timing. Slow processes like spike-frequency adaptation are shown to be a key feature in this context since they are necessary for the model to connect between different driving regimes.