Stimulus onset quenches neural variability: a widespread cortical phenomenon

By Mark M. Churchland, Byron M. Yu, John P. Cunningham, L. Sugrue, Marlene R. Cohen, Greg Corrado, William T. Newsome, Andrew M Clark, Paymon Hosseini, B. B. Scott and 16 more. Nature Neuroscience, 2010

1,116 citations recorded by the source corpus.

Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/54e69e627321894f5ae66bdb6159fbbed1f6bcec

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Citations in the corpus

How advances in neural recording affect data analysis (2011, background): “, and results using a state-space approach have shown that stimulus onset reduces neural variability across cortex in a wide range of area”
Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks. (2017, neutral): “S4), and therefore always decrease the amount of variability in the dynamics (Churchland and al., 2010; Rajan et al., 2010).”
Cortical State and Attention (2011, neutral): “— a phenomenon that may be related to the suppression of firing variability by stimulus onset”
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011, result): “It is tempting to compare these results to the experimental finding of reduced firing rate variability after stimulus onset observed in several cortical areas [50].”
Deep neuroethology of a virtual rodent (2019, background): “This effect loosely resembles, and perhaps informs one line of interpretation of the widely reported phenomenon of neural variability reducing with stimulus or task onset (Churchland et al., 2010).”
Recurrent Network Models of Sequence Generation and Memory. (2016, background): “In experiments, strong inputs have also been shown to reduce the Fano factor and trial-to-trial variability associated with sponta-neous activity (Churchland et al., 2010; White et al., 2012).”
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014, background): “Moreover, it can also be shown that the posterior variance of u, represented by the variance of model neuron activities, captures important aspects of the across-trial variance of V1 responses [11], namely the quenching of neural variability with stimulus onset [45].”
Cortical areas interact through a communication subspace (2019, neutral): “, 2013), stimulus drive (Smith and Kohn, 2008; Churchland et al., 2010), and more.”
Cortical control of arm movements: a dynamical systems perspective. (2013, methodology): “However, some studies at the single-neuron level have come to the opposite conclusion: that preparatory and movement tuning are often dissimilar (e.g., Wise et al. 1986) and nearly uncorrelated on average (e.g., Churchland et al. 2010a, Crammond & Kalaska 2000, Kaufman et al. 2010, 2013).”