How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains

Ostojic, Srdjan; Brunel, Nicolas; Hakim, Vincent

doi:10.1523/jneurosci.1275-09.2009

Cited by 216 publications

(264 citation statements)

References 85 publications

(143 reference statements)

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“…However, cortical neurons are embedded in a complex network, and thus the impacts of surrounding local circuit also have to be considered to explain the interactions between the recorded pair of neurons (Zhou et al, 2008;Ostojic et al, 2009). Indeed, the present triphasic causality dynamics observed for cell pairs with a displaced CCG peak suggests a nonclassical view of the functional connectivity within a cortical local circuit.…”

Section: Cross-correlation and Granger Causalitymentioning

confidence: 88%

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Hirabayashi¹,

Takeuchi²,

Tamura³

et al. 2010

J. Neurosci.

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The functional connectivity between cortical neurons is not static and is known to exhibit contextual modulations in terms of the coupling strength. Here we hypothesized that the information flow in a cortical local circuit exhibits complex forward-and-back dynamics, and conducted Granger causality analysis between the neuronal spike trains that were simultaneously recorded from macaque inferior temporal (IT) cortex while the animals performed a visual object discrimination task. Spikes from neuron pairs with a displaced peak on the cross-correlogram (CCG) showed Granger causality in the gamma-frequency range (30 -80 Hz) with the dominance in the direction consistent with the CCG peak (forward direction). Although, in a classical view, the displaced CCG peak has been interpreted as an indicative of a pauci-synaptic serial linkage, temporal dynamics of the gamma Granger causality after stimulus onset exhibited a more complex triphasic pattern,withatransientforwardcomponentfollowedbyaslowlydevelopingbackwardcomponentandsubsequentreappearanceoftheforward component. These triphasic dynamics of causality were not explained by the firing rate dynamics and were not observed for cell pairs that exhibited a center peak on the CCG. Furthermore, temporal dynamics of Granger causality depended on the feature configuration within the presented object. Together, these results demonstrate that the classical view of functional connectivity could be expanded to incorporate more complex forward-and-back dynamics and also imply that multistage processing in the recognition of visual objects might be implemented by multiphasic dynamics of directional information flow between single neurons in a local circuit in the IT cortex.

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Section: Cross-correlation and Granger Causalitymentioning

confidence: 88%

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Hirabayashi¹,

Takeuchi²,

Tamura³

et al. 2010

J. Neurosci.

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“…Multiple mechanisms could account for the brain state dependence of inhibition, including short-term plasticity, differences in background noise, synaptic strength, membrane potentials, chloride reversal potential, and/or neuromodulator levels during theta and SO. It indicates that, in a permanent regime of brain activity, synaptic connections are differentially recruited (Ostojic et al, 2009). Our .…”

Section: Principal Cellsmentioning

confidence: 99%

Brain State Dependent Postinhibitory Rebound in Entorhinal Cortex Interneurons

Adhikari¹,

Quilichini²,

Roy³

et al. 2012

J. Neurosci.

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Postinhibitory rebound (PIR) is believed to play an important role in the genesis and maintenance of biological rhythms. While it has been demonstrated during several in vitro studies, in vivo evidence for PIR remains scarce. Here, we report that PIR can be observed in the dorsomedial entorhinal cortex of anesthetized rats, mostly between putatively connected GABAergic interneurons, and that it is more prevalent during the theta (4 -6 Hz) oscillation state than the slow (0.5-2 Hz) oscillation state. Functional inhibition was also found to be brain state and postsynaptic cell type dependent but that alone could not explain this brain state dependence of PIR. A theoretical analysis, using two Fitzhugh-Nagumo neurons coupled to an external periodic drive, predicted that the modulation of a faster spiking rate by the slower periodic drive could account for the brain state dependence of PIR. Model predictions were verified experimentally. We conclude that PIR is cell type and brain state dependent and propose that this could impact network synchrony and rhythmogenesis.

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“…This is so when input correlations are not too strong, otherwise the correlations become independent of firing rate (Tchumatchenko et al 2010). Ostojic et al (2009) studied the relationship between the synaptic connections and the correlations of the spike trains, and pointed out that common synaptic input could enhance the correlations of neural firing activity. Further, recent work stated that feed-forward inhibitory circuitry could help to prevent excessive correlations in the face of sensory-evoked activation leading to improved population coding (Middleton et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Effect of inhibitory feedback on correlated firing of spiking neural network

Xie

Wang

2013

Cogn Neurodyn

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Understanding the properties and mechanisms that generate different forms of correlation is critical for determining their role in cortical processing. Researches on retina, visual cortex, sensory cortex, and computational model have suggested that fast correlation with high temporal precision appears consistent with common input, and correlation on a slow time scale likely involves feedback. Based on feedback spiking neural network model, we investigate the role of inhibitory feedback in shaping correlations on a time scale of 100 ms. Notably, the relationship between the correlation coefficient and inhibitory feedback strength is non-monotonic. Further, computational simulations show how firing rate and oscillatory activity form the basis of the mechanisms underlying this relationship. When the mean firing rate holds unvaried, the correlation coefficient increases monotonically with inhibitory feedback, but the correlation coefficient keeps decreasing when the network has no oscillatory activity. Our findings reveal that two opposing effects of the inhibitory feedback on the firing activity of the network contribute to the non-monotonic relationship between the correlation coefficient and the strength of the inhibitory feedback. The inhibitory feedback affects the correlated firing activity by modulating the intensity and regularity of the spike trains. Finally, the non-monotonic relationship is replicated with varying transmission delay and different spatial network structure, demonstrating the universality of the results.

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How Connectivity, Background Activity, and Synaptic Properties Shape the Cross-Correlation between Spike Trains

Cited by 216 publications

References 85 publications

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Triphasic Dynamics of Stimulus-Dependent Information Flow between Single Neurons in Macaque Inferior Temporal Cortex

Brain State Dependent Postinhibitory Rebound in Entorhinal Cortex Interneurons

Effect of inhibitory feedback on correlated firing of spiking neural network

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