Correlations and Neuronal Population Information

Kohn, Adam; Coen-Cagli, Ruben; Kanitscheider, Ingmar; Pouget, Alexandre

doi:10.1146/annurev-neuro-070815-013851

Cited by 356 publications

(475 citation statements)

References 131 publications

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“…The presence and structure of spontaneous correlations in a network can also limit its representational capacity (30,31). Indeed, humans exhibit striking capacity limits in attention and working memory (32), and these constraints are related to general cognitive abilities (33), including the active representation and evaluation of rules in dynamic environments (34).…”

Section: Discussionmentioning

confidence: 99%

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Waskom

Wagner

2017

Proc. Natl. Acad. Sci. U.S.A.

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Human prefrontal cortex supports goal-directed behavior by representing abstract information about task context. The organizational basis of these context representations, and of representations underlying other higher-order processes, is unknown. Here, we use multivariate decoding and analyses of spontaneous correlations to show that context representations are distributed across subnetworks within prefrontal cortex. Examining targeted prefrontal regions, we found that pairs of voxels with similar context preferences exhibited spontaneous correlations that were approximately twice as large as those between pairs with opposite context preferences. This subnetwork organization was stable across task-engaged and resting states, suggesting that abstract context representations are constrained by an intrinsic functional architecture. These results reveal a principle of fine-scaled functional organization in association cortex.fMRI | resting-state | rule | cognitive control | functional organization T he cerebral cortex exhibits functional organization at multiple spatial scales. At a coarse scale, the cortex is parcellated into functional areas (1, 2) that coordinate as networks through longrange connections (3-5). These areas represent and compute information with functional circuitry that is organized more finely. Some fine-scaled, intrinsic principles have been described in detail, particularly for sensory cortex (6, 7). In contrast, the subregional organization of prefrontal cortex (PFC), which gives rise to higherorder processes, including attention, decision-making, and goaldirected action (8, 9), remains largely uncharacterized. Whether PFC representations are encoded within an equipotential system or constrained by an intrinsic functional architecture is currently unknown.Sensory cortex can be mapped by parametrically varying stimulus attributes and measuring changes in the neural response, but complex and dynamic response properties limit the effectiveness of this approach for mapping PFC and other association regions. An alternate strategy is to leverage spontaneous variability in neural activity. Neural responses to repeated presentations of sensory stimuli, or in the absence of stimulation and explicit task demands, exhibit variability that is attributed to ongoing spontaneous activity (10-13). Traditional analyses consider spontaneous activity to be noise, but there is increasing evidence that shared spontaneous variability is a signature of functional organization (14). Analyses of spontaneous correlations have been used to identify multiple large-scale functional networks (4,5,15) and boundaries between functional areas (2, 16-18) in human and nonhuman primate association cortex. The spontaneous correlation structure also mirrors established fine-scaled principles of functional organization in visual cortex, such as preferences for retinotopic position (19) and stimulus orientation (20). We therefore leveraged spontaneous activity to examine the finescaled functional organization of human PFC.We focus...

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Section: Discussionmentioning

confidence: 99%

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Waskom

Wagner

2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Finally, if the two neurons have zero signal correlation, then any noise correlation will be beneficial (Abbott & Dayan 1999, Averbeck & Lee 2006, Averbeck et al 2006, Chen et al 2006, Kohn et al 2016). The analysis of noise correlations and their impact on encoding and decoding is further complicated by the findings that these correlations can be (modestly) modulated by the stimulus (Kohn et al 2016), by the task that the subject performs (Cohen & Newsome 2008, Bondy et al, 2018) and by cognitive factors such as attention (Cohen & Maunsell 2009, Mitchell et al 2009). Better understanding of actual decoders and better demarcation of the temporal intervals over which a perceptual decision is formed are necessary for determining the impact of noise correlations (and their modulations) on perception.…”

Section: Decoding Neural Responsesmentioning

confidence: 99%

Linking V1 Activity to Behavior

Seidemann

Geisler

2018

Annu. Rev. Vis. Sci.

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A long-term goal of visual neuroscience is to develop and test quantitative models that account for the moment-by-moment relationship between neural responses in early visual cortex and human performance in natural visual tasks. This review focuses on efforts to address this goal by measuring and perturbing the activity of primary visual cortex (V1) neurons while non- human primates perform demanding, well-controlled visual tasks. We start by describing a conceptual approach—the decoder linking model (DLM) framework—in which candidate decoding models take neural responses as input and generate predicted behavior as output. The ultimate goal in this framework is to find the actual decoder—the model that best predicts behavior from neural responses. We discuss key relevant properties of primate V1 and review current literature from the DLM perspective. We conclude by discussing major technological and theoretical advances that are likely to accelerate our understanding of the link between V1 activity and behavior.

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“…In primates performing visual tasks, selective attention strongly modulates cortical state (Engel et al, 2016) and population correlations (Kohn et al, 2016;Nienborg et al, 2012). Reduction of correlated activity (decorrelation) best accounts for perceptual improvements in these tasks (Cohen and Maunsell, 2009), but the neuronal subtypes involved remain unclear.…”

Section: Introductionmentioning

confidence: 99%

Cortical states control visual spatial perception

Speed

Rosario

Burgess

et al. 2018

Preprint

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SummaryMany factors modulate the state of cortical activity, but the importance of cortical states for sensory perception remains debated.We trained mice to detect spatially localized visual stimuli, and simultaneously measured local field potentials and excitatory and inhibitory neuron populations across layers of primary visual cortex (V1). Cortical states with low firing rates and correlations between excitatory neurons, and reduced oscillatory activity in Layer 4, accurately predicted single trials of visual spatial detection behavior. Our results show that cortical states exert strong effects at the initial stage of cortical processing in V1, and play a decisive role for visual spatial behavior in mice.

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Correlations and Neuronal Population Information

Cited by 356 publications

References 131 publications

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Distributed representation of context by intrinsic subnetworks in prefrontal cortex

Linking V1 Activity to Behavior

Cortical states control visual spatial perception

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