Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex

Abstract

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 (ϳ3 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.

Stochastic transitions into silence cause noise correlations in cortical circuits

“…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.…”

“…To describe the fluctuations of r(t), we used a rate model with adaptation a(t) where the dynamics were given by the following (22,25,28): τ r dr dt = −rðtÞ + ϕðαrðtÞ − aðtÞ + IðtÞ − θÞ, τ a da dt = −aðtÞ + βrðtÞ, [2] where θ = 2 was the activation threshold and the external input I(t) = I + stim(t) + σξ(t) was composed of constant term I (range, 0-4 a.u. ), the stimulus step function (amplitude, 60 a.u.…”

“…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.…”

“…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.…”

Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep

“…However, the network interaction between these types of morphofunctional discriminated units has still largely not been investigated. Neuronal correlations have been shown to decay with space in primary visual cortex, possibly caused by the highly structured nature of inputs (18). In parallel, it has been shown that, in the rodent hippocampus, at the top of cortical processing, such an effect of distance on neuronal correlations was also present and was different for excitatory and inhibitory cells (19).…”