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

By Matteo Carandini. 2004

211 citations recorded by the source corpus.

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.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Normalization as a canonical neural computation (2011, background): “In neurons such as those in area V1, the resulting fluctuations in membrane potential are essential in making the neuron fire: without them, many stimuli would evoke membrane potential fluctuations that are too small to reach spike threshol”
When size matters: attention affects performance by contrast or response gain (2010, background): “, we must also consider the possibility that performance is instead limited by the Poisson-like noise evident in single-cell firing rate”
Melting the iceberg: contrast invariance in visual cortex. (2007, background): “Moreover, noise in membrane potential is independent of the mean depolarization (Carandini, 2004a), so it comes as an intriguing surprise to see that it decreases with contrast.”
Measuring and interpreting neuronal correlations (2011, unknown): “The shape of the nonlinearity is not critical; ours was chosen so that the variance of the spiking response is nearly the same as the mean for both weak and strong response”
The Operating Point of the Cortex: Neurons as Large Deviation Detectors (2007, background): “1) (Granit et al., 1963; Deboer and Kuyper, 1968; Lankheet et al., 1989; Carandini, 2004).”
GABAA inhibition controls response gain in visual cortex (2011, unknown): “Intracellular measurements support this notion but also indicate that noise in the membrane potential (which we ignored) smoothes the threshold and turns it into an exponent (Carandini and Ferster, 2000; Carandini, 2004; Priebe and Ferster, 2008).”
Stimulus onset quenches neural variability: a widespread cortical phenomenon (2010, methodology): “The Fano factor has been used extensively to characterize neural variability (for example, see refs”
Mechanisms underlying cross-orientation suppression in cat visual cortex (2006, background): “and the amplification of the trial-to-trial variability of response amplitud”
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. (2009, background): “Uncorrelated noise, unique to each neuron’s response, can arise from variability in synaptic transmission that is amplified by the threshold nonlinearity in spike generation (Calvin and Stevens, 1967; Carandini, 2004).”
The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex. (2007, background): “The parameters of the power law are mainly determined by (1) the resting membrane potential of the cell, (2) the cell’s threshold, and (3) the amount of noise or trial-to-trial variability in the membrane-potential responses (Carandini, 2004; Chance et al., 2002; Hansel and van Vreeswijk, 2002; Mill”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, background): “In cat and monkey primary visual cortex, Fano factor is 1 for neurons recorded from anesthetized (153, 884, 957, 958) and awake (430, 742, 883, 1014) animals (but see Refs.”