Nonlinear Transfer of Signal and Noise Correlations in Cortical Networks

Abstract

Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs. We characterize these effects systematically in vitro in mice and demonstrate their impact on sensory processing in vivo in gerbils. We also find that the effects of nonlinear correlation transfer on cortical responses are stronger in the synchronized state than in the desynchronized state, and show that they can be reproduced and understood in a model with a simple threshold nonlinearity. Since these effects arise from an intrinsic neuronal property, they are likely to be present across sensory systems and, thus, our results are a critical step toward a general understanding of how correlated spiking relates to the structure and function of cortical networks.

The mechanics of state-dependent neural correlations

“…Formally, the nonlinear transfer between continuous input and spike response ensures that ρ < ρ x 92,37,39,93,94 , yet the influence of the nonlinearity can be controlled by several factors. In many neuron models L increases with the firing rate of a neuron resulting in a relationship between firing rates and ρ 37,38,95 . This prediction has been verified in a variety of experimental studies 25,96,10,95 , and firing rate is often a core determinant of output correlation 45 .…”

“…In many neuron models L increases with the firing rate of a neuron resulting in a relationship between firing rates and ρ 37,38,95 . This prediction has been verified in a variety of experimental studies 25,96,10,95 , and firing rate is often a core determinant of output correlation 45 . However, in general, output spiking correlations and firing rate do not have a prescribed relation, as our examples illustrate (Fig.…”

Inhibitory control of correlated intrinsic variability in cortical networks

“…While a number of modeling studies have explored the impact of correlations on sensory coding (Shadlen et al, 1996; de la Rocha et al, 2007; Averbeck et al, 2006; Pillow et al, 2008; Ecker et al, 2011; Moreno-Bote et al, 2014), there have been few efforts to identify their biophysical origin; the standard assumption that correlations arise from common input noise (de la Rocha et al, 2007; Doiron et al, 2016; Lyamzin et al, 2015) simply pushes the correlations from spiking to the membrane voltage without providing insight into their genesis. Models that use external noise to create correlations have been used in theoretical investigations of how network dynamics can transform correlations (Doiron et al, 2016), but no physiological source for the external noise used in these models has yet been identified.…”

Response variability and population coupling of cortical synaptic inputs are strongly influenced by network properties

“…Of course, each neuron is also a contributing member of the network. And although the relationship is complex (Carandini 2004), the nature of the synaptic inputs is likely extremely relevant to that of output spiking activity (Doiron et al 2016;Litwin-Kumar et al 2011;Lyamzin et al 2015). We therefore hypothesize that the broad g-LFP coupling distribution observed here corresponds to the diverse spike-spike population coupling observed elsewhere.…”