Spatial and temporal coherence in cortico-cortical connections: A cross-correlation study in areas 17 and 18 in the cat

Nelson, J. I.; Salin, P. A.; Munk, Mhj; Arzi, M.; Bullier, Jean

doi:10.1017/s0952523800006349

Cited by 179 publications

(124 citation statements)

References 68 publications

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“…The peaked cross-correlograms shown in the figure were obtained with ϭ 0.1, which is roughly comparable with the measured probability that two nearby pyramidal neurons are connected (Braitenberg and Schüz, 1997). Larger peaks, reflecting stronger synchronization, are common in correlograms constructed from experimental data (Fetz et al, 1991;Nelson et al, 1992).…”

Section: Impact Of Correlations Generated By Common Drivesupporting

confidence: 78%

“…At the moment, experimental data seem insufficient to determine this. Based on anatomical considerations (White, 1989;Braitenberg and Schüz, 1997) and neurophysiological measurements (Fetz et al, 1991;Nelson et al, 1992;Zohary et al, 1994;Salinas et al, 2000), it seems likely that all terms are different from zero, at least for local microcircuits; but what really needs to be known is the final weighted sum. This final sum might not be constant, neither in time nor across cortical areas.…”

Section: Discussionmentioning

confidence: 99%

“…We used two methods to generate temporal dependencies between inputs, common drive and temporal comodulations in rate (Brody, 1999). In both cases we computed cross-correlation histograms (Perkel et al, 1967;Fetz et al, 1991;Nelson et al, 1992;Brody, 1999) to visualize the resulting dependencies between pairs of input spike trains. For this, we recorded the firing times of two of the inputs, a and b, for a period of time of length T max .…”

Section: Calculation Of Cross-correlation Histogramsmentioning

confidence: 99%

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Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

2000

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Cortical neurons are typically driven by thousands of synaptic inputs. The arrival of a spike from one input may or may not be correlated with the arrival of other spikes from different inputs. How does this interdependence alter the probability that the postsynaptic neuron will fire? We constructed a simple random walk model in which the membrane potential of a target neuron fluctuates stochastically, driven by excitatory and inhibitory spikes arriving at random times. An analytic expression was derived for the mean output firing rate as a function of the firing rates and pairwise correlations of the inputs. This stochastic model made three quantitative predictions. (1) Correlations between pairs of excitatory or inhibitory inputs increase the fluctuations in synaptic drive, whereas correlations between excitatory-inhibitory pairs decrease them. (2) When excitation and inhibition are fully balanced (the mean net synaptic drive is zero), firing is caused by the fluctuations only. (3) In the balanced case, firing is irregular. These theoretical predictions were in excellent agreement with simulations of an integrate-and-fire neuron that included multiple conductances and received hundreds of synaptic inputs. The results show that, in the balanced regime, weak correlations caused by signals shared among inputs may have a multiplicative effect on the input-output rate curve of a postsynaptic neuron, i.e. they may regulate its gain; in the unbalanced regime, correlations may increase firing probability mainly around threshold, when output rate is low; and in all cases correlations are expected to increase the variability of the output spike train. Key words: random-walk; integrate-and-fire; computer simulation; spike synchrony; oscillations; cross-correlation; balanced inhibition; cerebral cortexThe output of a typical cortical neuron depends on the activity of a large number of synaptic inputs-several thousands of them, as estimated by anatomical techniques (White, 1989; Braitenberg and Shüz, 1997). What kind of response should be expected from a postsynaptic neuron driven by so many inputs? Answering this question in detail requires a deep understanding of dendritic integration, synaptic function, and spike generation mechanisms; but, given the large numbers commonly involved, as a first approximation it is natural to cast the problem in statistical terms. The strategy then is to compute the output responses of a model neuron (or their statistics), given a set of driving inputs with known statistical properties. These inputs may be either independent or temporally correlated. In the latter case, spikes from different input neurons arrive close together in time more often or less often than expected by chance.In general, the situation with independent inputs is easier to analyze, and for many applications it is probably a good approximation. However, there are at least three reasons why the effects of correlations on single cells should be fully characterized. First, correlations in spike counts have indeed been o...

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Section: Impact Of Correlations Generated By Common Drivesupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Calculation Of Cross-correlation Histogramsmentioning

confidence: 99%

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Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

2000

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“…The finding that synergy is strongest for pairs with dissimilar tuning may appear surprising because correlated firing, and sharp synchrony in particular, are strongest between nearby neurons that have similar tuning (Nelson et al, 1992;Lee et al, 1998;De Angelis et al, 1999;Nowak et al, 1999;Bair et al, 2001;Kohn and Smith, 2005). Our analysis reveals, however, that synergy is strongest between pairs of neurons for which correlation would be expected to be relatively weak.…”

Section: Greater Separation In Direction Preference Leads To Synergymentioning

confidence: 53%

The Role of Correlations in Direction and Contrast Coding in the Primary Visual Cortex

Montani

Kohn²,

et al. 2007

J. Neurosci.

114

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The spiking activity of nearby cortical neurons is not independent. Numerous studies have explored the importance of this correlated responsivity for visual coding and perception, often by comparing the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. Pairwise responses typically provide slightly more information so that encoding is weakly synergistic. The simple comparison between pairwise and summed individual responses conflates several forms of correlation, however, making it impossible to judge the relative importance of synchronous spiking, basic tuning properties, and stimulus-independent and stimulus-dependent correlation. We have applied an information theoretic approach to this question, using the responses of pairs of neurons to drifting sinusoidal gratings of different directions and contrasts that have been recorded in the primary visual cortex of anesthetized macaque monkeys. Our approach allows us to break down the information provided by pairs of neurons into a number of components. This analysis reveals that, although synchrony is prevalent and informative, the additional information it provides frequently is offset by the redundancy arising from the similar tuning properties of the two cells. Thus coding is approximately independent with weak synergy or redundancy arising, depending on the similarity in tuning and the temporal precision of the analysis. We suggest that this would allow cortical circuits to enjoy the stability provided by having similarly tuned neurons without suffering the penalty of redundancy, because the associated information transmission deficit is compensated for by stimulus-dependent synchrony.

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“…Because our primary objective was to measure the stimulus dependence of neuronal correlation, we recorded from nearby neurons (typically Ͻ500 m apart) that had similar receptive field properties, because distant or dissimilar neurons tend to fire independently (Nelson et al, 1992;Lee et al, 1998;DeAngelis et al, 1999;Nowak et al, 1999;Bair et al, 2001) and have weak correlation in response variability (Zohary et al, 1994, Lee et al, 1998Bair et al, 2001;Averbeck and Lee, 2003). We recorded in all cortical layers but biased our population toward complex neurons (76% of the population) (Skottun et al, 1991).…”

Section: Resultsmentioning

confidence: 99%

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

Kohn¹,

Ma²

2005

J. Neurosci.

512

584

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Nearby cortical neurons often have correlated trial-to-trial response variability, and a significant fraction of their spikes occur synchronously. These two forms of correlation are both believed to arise from common synaptic input, but the origin of this input is unclear. We investigated the source of correlated responsivity by recording from pairs of single neurons in primary visual cortex of anesthetized macaque monkeys and comparing correlated variability and synchrony for spontaneous activity and activity evoked by stimuli of different orientations and contrasts. These two stimulus manipulations would be expected to have different effects on the cortical pool providing input to the recorded pair: changing stimulus orientation should recruit different populations of cells, whereas changing stimulus contrast affects primarily the relative strength of sensory drive and ongoing cortical activity. Consistent with this predicted difference, we found that correlation was affected by these stimulus manipulations in different ways. Synchrony was significantly stronger for orientations that drove both neurons well than for those that did not, but correlation on longer time scales was orientation independent. Reducing stimulus contrast resulted in a decrease in the temporal precision of synchronous firing and an enhancement of correlated response variability on longer time scales. Our results thus suggest that correlated responsivity arises from mechanisms operating at two distinct timescales: one that is orientation tuned and that determines the strength of temporally precise synchrony, and a second that is contrast sensitive, of low temporal frequency, and present in ongoing cortical activity.

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Spatial and temporal coherence in cortico-cortical connections: A cross-correlation study in areas 17 and 18 in the cat

Cited by 179 publications

References 68 publications

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models

The Role of Correlations in Direction and Contrast Coding in the Primary Visual Cortex

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

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