Predicting spike timing of neocortical pyramidal neurons by simple threshold 
models

Jolivet, R; Rauch, A; Lüscxher, H-R; Gerstner, W

doi:10.1007/s10827-006-7074-5

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Predicting spike timing of neocortical pyramidal neurons by simple threshold models

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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., Lüscxher, H.-R., & Gerstner, W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. Journal of Computational Neuroscience, 21(1), 35-49. doi:10.1007/s10827-006-7074-5.

Cite as: https://hdl.handle.net/21.11116/0000-0004-836D-E

Abstract

Neurons generate spikes reliably with millisecond precision if driven by a fluctuating current—is it then possible to predict the spike timing knowing the input? We determined parameters of an adapting threshold model using data recorded in vitro from 24 layer 5 pyramidal neurons from rat somatosensory cortex, stimulated intracellularly by a fluctuating current simulating synaptic bombardment in vivo. The model generates output spikes whenever the membrane voltage (a filtered version of the input current) reaches a dynamic threshold. We find that for input currents with large fluctuation amplitude, up to 75% of the spike times can be predicted with a precision of ±2 ms. Some of the intrinsic neuronal unreliability can be accounted for by a noisy threshold mechanism. Our results suggest that, under random current injection into the soma, (i) neuronal behavior in the subthreshold regime can be well approximated by a simple linear filter; and (ii) most of the nonlinearities are captured by a simple threshold process.