Spatial and temporal scales of neuronal correlation in primary visual cortex

By Matthew A. Smith, Adam Kohn. Journal of Neuroscience, 2008

606 citations recorded by the source corpus.

The spiking activity of cortical neurons is correlated. For instance, trial-to-trial fluctuations in response strength are shared between neurons, and spikes often occur synchronously. Understanding the properties and mechanisms that generate these forms of correlation is critical for determining their role in cortical processing. We therefore investigated the spatial extent and functional specificity of correlated spontaneous and evoked activity. Because feedforward, recurrent, and feedback pathways have distinct extents and specificity, we reasoned that these measurements could elucidate the contribution of each type of input. We recorded single unit activity with microelectrode arrays which allowed us to measure correlation in many hundreds of pairings, across a large range of spatial scales. Our data show that correlated evoked activity is generated by two mechanisms that link neurons with similar orientation preferences on different spatial scales: one with high temporal precision and a limited spatial extent (∼3 mm), and a second that gives rise to correlation on a slow time scale and extends as far as we were able to measure (10 mm). The former is consistent with common input provided by horizontal connections; the latter likely involves feedback from extrastriate cortex. Spontaneous activity was correlated over a similar spatial extent, but approximately twice as strongly as evoked activity. Visual stimuli thus caused a substantial decrease in correlation, particularly at response onset. These properties and the circuit mechanism they imply provide new constraints on the functional role that correlation may play in visual processing.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d34d1de4e263a314aebd9a18c7d6683dcfcab71c

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Citations in the corpus

How advances in neural recording affect data analysis (2011, methodology): “In addition, spike data from 106 primary visual cortical neurons were recorded while an anesthetized monkey viewed oriented grating”
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. (2009, methodology): “Correlations are not limited to local populations but persist even between neurons separated as much as 10 mm in cortex (Smith and Kohn, 2008; Nauhaus et al., 2009).”
Spontaneous and driven cortical activity: implications for computation. (2009, positive): “The interaction between the input and the ongoing cortical state depends on the strength of the feed-forward thalamic signals [9,12 ,30,31].”
Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition (2011, background): “Although the impact of correlation changes on sensory coding has been extensively discussed (18, 27, 32–35, 48–50), few studies have measured spike train correlation across timescales (51).”
Cortical areas interact through a communication subspace (2019, positive): “, 2013), stimulus drive (Smith and Kohn, 2008; Churchland et al., 2010), and more.”
Stimulus onset quenches neural variability: a widespread cortical phenomenon (2010, result): “The decline in rate covariance is consistent with a previous stud”
Spike Synchrony Reveals Emergence of Proto-Objects in Visual Cortex (2015, background): “Synchronous firing in widely separated neurons Experiments in anesthetized animals (where feedback from higher levels is presumably reduced or absent) have shown that such tight synchrony falls off rapidly with distance between neurons, reaching zero at 4 mm (Smith and Kohn, 2008; for LFP and MUA co”
Measuring and interpreting neuronal correlations (2011, background): “They tend to be highest for pairs of neurons that are near each othe”
Correlations of neural predictability and information transfer in cortex and their relation to predictive coding (2025, unknown): “To compute the cross correlation, we perform a shuffling across trials in order to remove correlations that are locked to the stimulus [26], i.e. we compute shuffle-corrected CCGs.”