Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque

Kohn, Adam; Ma, Smith

doi:10.1523/jneurosci.5106-04.2005

Cited by 512 publications

(697 citation statements)

References 74 publications

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“…4C). Consistent with experimental data (18,29), we found that the correlations between neurons decayed exponentially with the difference in the cells' preferred orientation (Fig. S2).…”

Section: Materials and Methods)supporting

confidence: 89%

“…It has been demonstrated that population coding depends not only on the shape of the neurons' tuning curves but also on the distribution of neuronal correlations across the network (11)(12)(13)16). Indeed, during the past decade, it has become increasingly understood that the trial-by-trial variability in neuronal responses, or ''noise,'' is not independent, but exhibits correlations (17,18).…”

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confidence: 99%

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Efficient coding in heterogeneous neuronal populations

Chelaru

Dragoi

2008

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

144

148

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A ubiquitous feature of neuronal responses within a cortical area is their high degree of inhomogeneity. Even cells within the same functional column are known to have highly heterogeneous response properties when the same stimulus is presented. Whether the wide diversity of neuronal responses is an epiphenomenon or plays a role for cortical function is unknown. Here, we examined the relationship between the heterogeneity of neuronal responses and population coding. Contrary to our expectation, we found that the high variability of intrinsic response properties of individual cells changes the structure of neuronal correlations to improve the information encoded in the population activity. Thus, the heterogeneity of neuronal responses is in fact beneficial for sensory coding when stimuli are decoded from the population response.computational modeling ͉ information coding ͉ sensory cortex ͉ visual cortex ͉ neuronal populations

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“…4C). Consistent with experimental data (18,29), we found that the correlations between neurons decayed exponentially with the difference in the cells' preferred orientation (Fig. S2).…”

Section: Materials and Methods)supporting

confidence: 89%

mentioning

confidence: 99%

Efficient coding in heterogeneous neuronal populations

Chelaru

Dragoi

2008

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

144

148

View full text Add to dashboard Cite

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“…We further assume neurons to have weakly correlated trial-to-trial variability in their spike counts, in line with numerous electrophysiological observations (e.g., Kohn & Smith, 2005;see Cohen & Kohn, 2011, for a recent review), although these findings have recently been contested (Ecker et al, 2010). Such correlated response fluctuations are thought to reflect the functional connectivity of neural circuits.…”

Section: Sensory Noisesupporting

confidence: 70%

A neural population model for visual pattern detection.

Goris

Putzeys

Wagemans³

et al. 2013

Psychological Review

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Pattern detection is the bedrock of modern vision science. Nearly half a century ago, psychophysicists advocated a quantitative theoretical framework that connected visual pattern detection with its neurophysiological underpinnings. In this theory, neurons in primary visual cortex constitute linear and independent visual channels whose output is linked to choice behavior in detection tasks via simple read-out mechanisms. This model has proven remarkably successful in accounting for threshold vision. It is fundamentally at odds, however, with current knowledge about the neurophysiological underpinnings of pattern vision. In addition, the principles put forward in the model fail to generalize to suprathreshold vision or perceptual tasks other than detection. We propose an alternative theory of detection in which perceptual decisions develop from maximum-likelihood decoding of a neurophysiologically inspired model of population activity in primary visual cortex. We demonstrate that this theory explains a broad range of classic detection results. With a single set of parameters, our model can account for several summation, adaptation, and uncertainty effects, thereby offering a new theoretical interpretation for the vast psychophysical literature on pattern detection.

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“…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.…”

mentioning

confidence: 97%

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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Stimulus Dependence of Neuronal Correlation in Primary Visual Cortex of the Macaque

Cited by 512 publications

References 74 publications

Efficient coding in heterogeneous neuronal populations

Efficient coding in heterogeneous neuronal populations

A neural population model for visual pattern detection.

Stochastic transitions into silence cause noise correlations in cortical circuits

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