Selective modulation of cortical state during spatial attention

Engel, Tatiana A.; Steinmetz, Nicholas A.; Gieselmann, Marc Alwin; Thiele, Alexander; Moore, Tirin; Boahen, Kwabena

doi:10.1126/science.aag1420

Cited by 207 publications

(309 citation statements)

References 59 publications

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“…Further, these gain fluctuations are well approximated by a one-dimensional, global stochastic process affecting all neurons in the population (Ecker et al, 2014; Rabinowitz et al, 2015; Lin et al, 2015; Ecker et al, 2016; Engel et al, 2016; Whiteway and Butts, 2017). When these techniques are applied to population recordings subject to attentional modulation, the global gain fluctuations are considerably reduced in the attended state (Rabinowitz et al, 2015; Ecker et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…In recent years there has been increased focus on how brain states affect trial-to-trial spiking variability (Crochet et al, 2011; Lin et al, 2015; Doiron et al, 2016; Stringer et al, 2016). In particular, attention decreases the shared variability (noise correlations) of the firing rates from pairs of neurons (Cohen and Maunsell, 2009; Mitchell et al, 2009; Cohen and Maunsell, 2011; Herrero et al, 2013; Ruff and Cohen, 2014; Engel et al, 2016). The combination of a reduction in noise correlations and an increase in response gain has potentially important functional consequences through an improved population code (Cohen and Maunsell, 2009; Rabinowitz et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Attentional modulation of neuronal variability in circuit models of cortex

Kanashiro

Ocker

Cohen

et al. 2017

eLife

101

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The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition.DOI: http://dx.doi.org/10.7554/eLife.23978.001

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attentional modulation of neuronal variability in circuit models of cortex

Kanashiro

Ocker

Cohen

et al. 2017

eLife

101

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“…spatial location or target in a mixture), like many visual studies (e.g. [51]), and one olfactory study (see [52]). Second, while an odor on/off design would have matched the tone on/off structure, it would have resulted in half the number of trials and cell-odor pairs to analyze.…”

Section: Discussionmentioning

confidence: 99%

Selective Attention Controls Olfactory Decisions and the Neural Encoding of Odors

et al. 2018

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Critical animal behaviors, especially among rodents, are guided by odors in remarkably well-coordinated manners, yet many extramodal sensory cues compete for cognitive resources in these ecological contexts. That rodents can engage in such odor-guided behaviors suggests that they can selectively attend to odors. Indeed, higher-order cognitive processes-such as learning, memory, decision making, and action selection-rely on the proper filtering of sensory cues based on their relative salience. We developed a behavioral paradigm to reveal that rats are capable of selectively attending to odors in the presence of competing extramodal stimuli. We found that this selective attention facilitates accurate odor-guided decisions, which become further strengthened with experience. Further, we uncovered that selective attention to odors adaptively sharpens their representation among neurons in the olfactory tubercle, an olfactory cortex region of the ventral striatum that is considered integral for evaluating sensory information in the context of motivated behaviors. Odor-directed selective attention exerts influences during moments of heightened odor anticipation and enhances odorant representation by increasing stimulus contrast in a signal-to-noise-type coding scheme. Together, these results reveal that rats engage selective attention to optimize olfactory outcomes. Further, our finding of attention-dependent coding in the olfactory tubercle challenges the notion that a thalamic relay is integral for the attentional control of sensory coding.

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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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Selective modulation of cortical state during spatial attention

Cited by 207 publications

References 59 publications

Attentional modulation of neuronal variability in circuit models of cortex

Attentional modulation of neuronal variability in circuit models of cortex

Selective Attention Controls Olfactory Decisions and the Neural Encoding of Odors

Cortical states control visual spatial perception

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