Neural Correlation Is Stimulus Modulated by Feedforward Inhibitory Circuitry

Middleton, Jason; Omar, Cyrus; Doiron, Brent; Simons, Daniel J.

doi:10.1523/jneurosci.3474-11.2012

Cited by 81 publications

(107 citation statements)

References 68 publications

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“…We next studied the dynamics of the population in response to short acoustic clicks (duration, 5 ms; interclick interval, 2.5 or 3.5 s). We used a sliding spike count window (T = 50 ms) and computed the averaged instantaneous rate, spike count correlation ρ(t) (2,14,15), and spike count Fano factor (11)(12)(13) by performing the statistics across repeated stimulus presentations and averaged over single units or single-unit pairs (Methods). Similarly, we computed the instantaneous silence density S(t) using 20-ms bins.…”

Section: Resultsmentioning

confidence: 99%

“…In anesthetized rodents correlations decrease with brain state desynchronization (8,9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11)(12)(13) seems to occur jointly with a transient decrease in correlation (2,14,15). These observations suggest that correlations reflect the dynamical state of the circuit more than its hardwired connectivity.…”

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confidence: 95%

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Stochastic transitions into silence cause noise correlations in cortical circuits

Mochol

Hermoso-Mendizabal

Sakata

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.neuronal variability | noise correlations | brain state | auditory cortex | stochastic network dynamics N euronal noise correlations are defined as common fluctuations in the spiking activity of neurons under conditions of constant sensory input or motor output. Traditionally, they have been thought to arise from the dense connectivity of the cortex, such that neighboring neurons sharing a fraction of their inputs should also share a fraction of their output variability (1). Several observations are consistent with this hypothesis: pairwise correlations in the cortex decrease with cell pair distance (2) or with the difference in stimulus selectivity (3), dependencies that could follow from a variation in shared input given the anatomy of cortical circuits. Recent findings, however, challenge this simple interpretation. Recordings in the primate visual cortex have shown that attention or task context can change correlation structure (4-6) and that the magnitude of averaged correlation can be very low (7). In anesthetized rodents correlations decrease with brain state desynchronization (8, 9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11-13) seems to occur jointly with a transient decrease in correlation (2, 14, 15). Th...

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 95%

Stochastic transitions into silence cause noise correlations in cortical circuits

Mochol

Hermoso-Mendizabal

Sakata

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Computational modeling has demonstrated that variability shaping can be caused by nonlinearities that alter the distribution of neural activity. Electrodes with positive signal correlations are decorrelated during stimulus representation because they receive a common feedforward input that changes the response distribution (Middleton et al, 2012). However, it is unclear how this change in the shape of variance on the scale of neurons would generalize to aggregate neural populations in ECoG.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Structure of Neural Variability in the Cortical Representation of Speech Sounds

Dichter

Bouchard

Chang

2016

Journal of Neuroscience

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Accurate sensory discrimination is commonly believed to require precise representations in the nervous system; however, neural stimulus responses can be highly variable, even to identical stimuli. Recent studies suggest that cortical response variability decreases during stimulus processing, but the implications of such effects on stimulus discrimination are unclear. To address this, we examined electrocorticographic cortical field potential recordings from the human nonprimary auditory cortex (superior temporal gyrus) while subjects listened to speech syllables. Compared with a prestimulus baseline, activation variability decreased upon stimulus onset, similar to findings from microelectrode recordings in animal studies. We found that this decrease was simultaneous with encoding and spatially specific for those electrodes that most strongly discriminated speech sounds. We also found that variability was predominantly reduced in a correlated subspace across electrodes. We then compared signal and variability (noise) correlations and found that noise correlations reduce more for electrodes with strong signal correlations. Furthermore, we found that this decrease in variability is strongest in the high gamma band, which correlates with firing rate response. Together, these findings indicate that the structure of single-trial response variability is shaped to enhance discriminability despite non-stimulus-related noise.

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“…Because of the thresholding associated with spike generation, the transfer of correlations from synaptic inputs to spiking output is highly nonlinear (Lampl et al, 1999;Binder and Powers, 2001;Dorn and Ringach, 2003;Shamir and Sompolinsky, 2004;de la Rocha et al, 2007;Shea-Brown et al, 2008;Burak et al, 2009;Tchumatchenko et al, 2010;Middleton et al, 2012). For example, even when total input correlation is constant, total output correlation can vary with changes in input strength, resulting in a positive dependency between the mean spike rate and total correlation in cortical spiking (de la Rocha et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

et al. 2015

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Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks.

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Neural Correlation Is Stimulus Modulated by Feedforward Inhibitory Circuitry

Cited by 81 publications

References 68 publications

Stochastic transitions into silence cause noise correlations in cortical circuits

Stochastic transitions into silence cause noise correlations in cortical circuits

Dynamic Structure of Neural Variability in the Cortical Representation of Speech Sounds

Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

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