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Amplification of Trial-to-Trial Response Variability by Neurons in Visual Cortex

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  • Matteo Carandini

Abstract

The visual cortex responds to repeated presentations of the same stimulus with high variability. Because the firing mechanism is remarkably noiseless, the source of this variability is thought to lie in the membrane potential fluctuations that result from summated synaptic input. Here this hypothesis is tested through measurements of membrane potential during visual stimulation. Surprisingly, trial-to-trial variability of membrane potential is found to be low. The ratio of variance to mean is much lower for membrane potential than for firing rate. The high variability of firing rate is explained by the threshold present in the function that converts inputs into firing rates. Given an input with small, constant noise, this function produces a firing rate with a large variance that grows with the mean. This model is validated on responses recorded both intracellularly and extracellularly. In neurons of visual cortex, thus, a simple deterministic mechanism amplifies the low variability of summated synaptic inputs into the large variability of firing rate. The computational advantages provided by this amplification are not known. A simple model accounts for the high variability of firing rates observed in responses of cortical neurons to visual stimuli.

Suggested Citation

  • Matteo Carandini, 2004. "Amplification of Trial-to-Trial Response Variability by Neurons in Visual Cortex," PLOS Biology, Public Library of Science, vol. 2(9), pages 1-1, August.
  • Handle: RePEc:plo:pbio00:0020264
    DOI: 10.1371/journal.pbio.0020264
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    References listed on IDEAS

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    1. Tal Kenet & Dmitri Bibitchkov & Misha Tsodyks & Amiram Grinvald & Amos Arieli, 2003. "Spontaneously emerging cortical representations of visual attributes," Nature, Nature, vol. 425(6961), pages 954-956, October.
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    Cited by:

    1. Medina, José M. & Díaz, José A., 2016. "Extreme reaction times determine fluctuation scaling in human color vision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 125-132.
    2. Lin, Lihui, 2021. "Does the procedure matter?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 90(C).
    3. Joseph A Lombardo & Matthew V Macellaio & Bing Liu & Stephanie E Palmer & Leslie C Osborne, 2018. "State dependence of stimulus-induced variability tuning in macaque MT," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-28, October.
    4. Sean T Kelly & Jens Kremkow & Jianzhong Jin & Yushi Wang & Qi Wang & Jose-Manuel Alonso & Garrett B Stanley, 2014. "The Role of Thalamic Population Synchrony in the Emergence of Cortical Feature Selectivity," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-13, January.
    5. Felipe Gerhard & Moritz Deger & Wilson Truccolo, 2017. "On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-31, February.

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