Stimulus onset quenches neural variability: A widespread cortical phenomenon

Department of Electrical Engineering, Stanford University School of Medicine, Stanford University, Stanford, California, USA.
Nature Neuroscience (Impact Factor: 16.1). 02/2010; 13(3):369-78. DOI: 10.1038/nn.2501
Source: PubMed


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.

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Available from: Matthew A Smith, Mar 27, 2014
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    • "Computational models of cortical networks typically derive irregular spiking by establishing a balance in fast E/I (Shadlen and Newsome, 1998;Renart et al., 2010), which allows inhibitory and excitatory currents to track each other closely in time, resulting in an active de-correlation in spiking (Renart et al., 2010). However, since independent, stationary Poisson processes are insufficient to explain the high variability of spiking (CV > 1) observed typically in vivo (Softky and Koch, 1993;Shadlen and Newsome, 1998), alternative mechanisms beyond external Poisson inputs (Brunel, 2000) have been proposed to further increase variability, such as intrinsic chaotic dynamics (VanVreeswijk and Sompolinsky, 1996;Sussillo and Abbott, 2009;Ostojic, 2014), conductance-based synapses (Kumar et al., 2008), clustered network architecture (Kumar and Doiron, 2012), external synchronous inputs (Stevens and Zador, 1998), and 'doubly stochastic' approaches using nonstationary Poisson processes (Churchland et al., 2010), among others. Our results confirm the high variability of single neuron firing in the AW state with CV > 1. "

    Full-text · Dataset · Jan 2016
    • "When visually stimulated, the neurons shift from the internally driven state to a state determined by the relationship between the stimulus properties and the neurons' tuning preferences. This transition is evidenced by a decrease in correlated variability between pairs of neurons during visual stimulation (Smith and Sommer 2013) and an increase in the signal reliability of individual neurons' responses (Churchland et al. 2010; Cohen and Maunsell 2009). We predict that this state transition during visual stimulation would also lead to weaker and less consistent coherence between spiking activity and the overlying EEG signals. "
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    • "For example, in the modeling work of Xing et al. (Xing et al. 2012) the amplitude of the gamma harmonic peaks strongly depended on the level of inputs to the simulated network. In parallel, higher activity leads to reduced slow fluctuations (Churchland et al. 2010), generating a concurrent reduction in power spectrum exponent. However, since the precise mechanisms that underlie these two phenomena are not fully clarified yet, the source of the link between them remains to be fully elucidated as well. "
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