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• W2921338417 abstract "•The state of activity in primary visual cortex (V1) influences perception in mice.•Low-frequency oscillations in layer 4 hinder stimulus detection.•Narrowband gamma oscillations in layer 4 promote stimulus detection.•These two key aspects of cortical states accurately predict single-trial behavior. Many factors modulate the state of cortical activity, but the importance of cortical state variability 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 spontaneous firing and correlations in excitatory neurons, and suppression of 3- to 7-Hz oscillations in layer 4, accurately predicted single-trial visual detection. Our results show that cortical states exert strong effects at the initial stage of cortical processing in V1 and can play a prominent role for visual spatial behavior in mice. Many factors modulate the state of cortical activity, but the importance of cortical state variability 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 spontaneous firing and correlations in excitatory neurons, and suppression of 3- to 7-Hz oscillations in layer 4, accurately predicted single-trial visual detection. Our results show that cortical states exert strong effects at the initial stage of cortical processing in V1 and can play a prominent role for visual spatial behavior in mice. Behavioral factors such as sleep, wakefulness, and movement have strong effects on the state of cortical activity. Cortical states are typically defined by the degree of shared fluctuations among cortical neural populations, measured by local field potential (LFP) frequency power (Harris and Thiele, 2011Harris K.D. Thiele A. Cortical state and attention.Nat. Rev. Neurosci. 2011; 12: 509-523Crossref PubMed Scopus (524) Google Scholar), and neural population correlations (Kohn et al., 2009Kohn A. Zandvakili A. Smith M.A. Correlations and brain states: from electrophysiology to functional imaging.Curr. Opin. Neurobiol. 2009; 19: 434-438Crossref PubMed Scopus (66) Google Scholar). Cortical states exert profound effects on sensory responses (Haider and McCormick, 2009Haider B. McCormick D.A. Rapid neocortical dynamics: cellular and network mechanisms.Neuron. 2009; 62: 171-189Abstract Full Text Full Text PDF PubMed Scopus (329) Google Scholar, Petersen and Crochet, 2013Petersen C.C. Crochet S. Synaptic computation and sensory processing in neocortical layer 2/3.Neuron. 2013; 78: 28-48Abstract Full Text Full Text PDF PubMed Scopus (157) Google Scholar), but there remain unresolved questions about cortical states and their effects on sensory perception. One question concerns the role of cortical states for perception across sensory modalities. In primates performing visual tasks, cortical states strongly influence stimulus detection (Gilbert and Li, 2013Gilbert C.D. Li W. Top-down influences on visual processing.Nat. Rev. Neurosci. 2013; 14: 350-363Crossref PubMed Scopus (608) Google Scholar, Reynolds and Chelazzi, 2004Reynolds J.H. Chelazzi L. Attentional modulation of visual processing.Annu. Rev. Neurosci. 2004; 27: 611-647Crossref PubMed Scopus (831) Google Scholar, Spitzer et al., 1988Spitzer H. Desimone R. Moran J. Increased attention enhances both behavioral and neuronal performance.Science. 1988; 240: 338-340Crossref PubMed Scopus (558) Google Scholar). However, recent studies in mice show that somatosensory perception occurs in a wide variety of cortical states—even those with correlations and activity patterns resembling slow-wave sleep (Sachidhanandam et al., 2013Sachidhanandam S. Sreenivasan V. Kyriakatos A. Kremer Y. Petersen C.C. Membrane potential correlates of sensory perception in mouse barrel cortex.Nat. Neurosci. 2013; 16: 1671-1677Crossref PubMed Scopus (226) Google Scholar). It is unknown how cortical states influence visual perception in mice and unclear whether the underlying mechanisms are like those in other mammalian visual systems or like those in other mouse sensory cortical areas. A second question concerns how cortical states coordinate excitatory and inhibitory neuron population activity during perception. In primates performing visual tasks, selective attention strongly modulates cortical state (Engel et al., 2016Engel T.A. Steinmetz N.A. Gieselmann M.A. Thiele A. Moore T. Boahen K. Selective modulation of cortical state during spatial attention.Science. 2016; 354: 1140-1144Crossref PubMed Scopus (104) Google Scholar) and population correlations (Kohn et al., 2016Kohn A. Coen-Cagli R. Kanitscheider I. Pouget A. Correlations and neuronal population information.Annu. Rev. Neurosci. 2016; 39: 237-256Crossref PubMed Scopus (161) Google Scholar, Nienborg et al., 2012Nienborg H. Cohen M.R. Cumming B.G. Decision-related activity in sensory neurons: correlations among neurons and with behavior.Annu. Rev. Neurosci. 2012; 35: 463-483Crossref PubMed Scopus (126) Google Scholar). Reduction of correlated activity (decorrelation) best accounts for perceptual improvements in these tasks (Cohen and Maunsell, 2009Cohen M.R. Maunsell J.H. Attention improves performance primarily by reducing interneuronal correlations.Nat. Neurosci. 2009; 12: 1594-1600Crossref PubMed Scopus (691) Google Scholar), but the neuronal subtypes involved remain unclear. Identification of cortical neuron subtypes in higher mammals presents challenges, since action potentials of many excitatory neurons are indistinguishable from those of inhibitory neurons (Constantinople et al., 2009Constantinople C.M. Disney A.A. Maffie J. Rudy B. Hawken M.J. Quantitative analysis of neurons with Kv3 potassium channel subunits, Kv3.1b and Kv3.2, in macaque primary visual cortex.J. Comp. Neurol. 2009; 516: 291-311Crossref PubMed Scopus (22) Google Scholar, Haider et al., 2010Haider B. Krause M.R. Duque A. Yu Y. Touryan J. Mazer J.A. McCormick D.A. Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation.Neuron. 2010; 65: 107-121Abstract Full Text Full Text PDF PubMed Scopus (186) Google Scholar, Soares et al., 2017Soares D. Goldrick I. Lemon R.N. Kraskov A. Greensmith L. Kalmar B. Expression of Kv3.1b potassium channel is widespread in macaque motor cortex pyramidal cells: a histological comparison between rat and macaque.J. Comp. Neurol. 2017; 525: 2164-2174Crossref PubMed Scopus (21) Google Scholar, Vigneswaran et al., 2011Vigneswaran G. Kraskov A. Lemon R.N. Large identified pyramidal cells in macaque motor and premotor cortex exhibit “thin spikes”: implications for cell type classification.J. Neurosci. 2011; 31: 14235-14242Crossref PubMed Scopus (113) Google Scholar). This may hinder full understanding of excitatory and inhibitory contributions to sensory perception. A third question concerns how cortical states affect information flow across cortical layers during sensory perception. A recent study of primate visual cortex area V4 revealed that cortical states underlying selective visual attention strongly modulate correlations in the input layers (Nandy et al., 2017Nandy A.S. Nassi J.J. Reynolds J.H. Laminar organization of attentional modulation in macaque visual area V4.Neuron. 2017; 93: 235-246Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar). In contrast, input layers in primary visual cortex (V1) exhibit low correlations and low sensitivity to cortical state changes (Hansen et al., 2012Hansen B.J. Chelaru M.I. Dragoi V. Correlated variability in laminar cortical circuits.Neuron. 2012; 76: 590-602Abstract Full Text Full Text PDF PubMed Scopus (98) Google Scholar, Poort et al., 2016Poort J. Self M.W. van Vugt B. Malkki H. Roelfsema P.R. Texture segregation causes early figure enhancement and later ground suppression in areas V1 and V4 of visual cortex.Cereb. Cortex. 2016; 26: 3964-3976Crossref PubMed Scopus (44) Google Scholar, Smith et al., 2013Smith M.A. Jia X. Zandvakili A. Kohn A. Laminar dependence of neuronal correlations in visual cortex.J. Neurophysiol. 2013; 109: 940-947Crossref PubMed Scopus (91) Google Scholar). It remains unknown how cortical states modulate activity across input and output layers of V1 during visual perception in mice. Several recent studies have provided insight about cortical states in awake mice. During quiet awake conditions, pupil fluctuations can index low- and high-arousal cortical states in the absence of overt behavioral changes (McGinley et al., 2015aMcGinley M.J. David S.V. McCormick D.A. Cortical membrane potential signature of optimal states for sensory signal detection.Neuron. 2015; 87: 179-192Abstract Full Text Full Text PDF PubMed Scopus (354) Google Scholar, Reimer et al., 2014Reimer J. Froudarakis E. Cadwell C.R. Yatsenko D. Denfield G.H. Tolias A.S. Pupil fluctuations track fast switching of cortical states during quiet wakefulness.Neuron. 2014; 84: 355-362Abstract Full Text Full Text PDF PubMed Scopus (347) Google Scholar, Vinck et al., 2015Vinck M. Batista-Brito R. Knoblich U. Cardin J.A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding.Neuron. 2015; 86: 740-754Abstract Full Text Full Text PDF PubMed Scopus (377) Google Scholar). These methods have revealed how changes in neuronal correlations, LFP spectral power, and membrane potential alter sensory responsiveness. These studies suggest that there are multiple ways that key features of cortical states in awake, non-behaving conditions could impact sensory signal reliability and coding (McGinley et al., 2015bMcGinley M.J. Vinck M. Reimer J. Batista-Brito R. Zagha E. Cadwell C.R. Tolias A.S. Cardin J.A. McCormick D.A. Waking state: rapid variations modulate neural and behavioral responses.Neuron. 2015; 87: 1143-1161Abstract Full Text Full Text PDF PubMed Scopus (373) Google Scholar). Importantly, only one of these studies examined features of cortical states and their relationship to perception, during an auditory task (McGinley et al., 2015aMcGinley M.J. David S.V. McCormick D.A. Cortical membrane potential signature of optimal states for sensory signal detection.Neuron. 2015; 87: 179-192Abstract Full Text Full Text PDF PubMed Scopus (354) Google Scholar). It still remains unknown how specific features of cortical states across layers of mouse V1 support visual perceptual behavior. To address these unresolved questions, we trained mice to detect visual stimuli appearing in discrete portions of the visual field and, simultaneously, measured LFP and excitatory and inhibitory neuron populations across layers of V1. Our approach used overt behavioral outcomes as criteria to identify the features of cortical states associated with accurate visual behavior. We then quantitatively assessed how effectively each of these features predicted stimulus detection on single trials across subjects. This approach revealed that in layer 4 (L4) of V1, enhanced narrowband gamma (50–70 Hz) LFP oscillations before the stimulus and suppressed low-frequency (3–7 Hz) oscillations during the stimulus accurately predicted single-trial visual detection. We designed a behavioral assay of visual spatial perception in stationary head-fixed mice (Figure 1A). Mice reported detection of visual stimuli by licking for water rewards. These were obtained only if they licked during the stimulus window (typically 1–1.5 s). Stimuli appeared only after a mandatory period of no licks had elapsed (typically 0.5–6 s, randomized per trial). Static horizontally oriented Gabor gratings appeared in one of two fixed spatial locations, either in the monocular or binocular visual fields. Gratings appeared at one of these locations for a block of 15–50 consecutive trials, and then switched to the other location for a new block of trials (Figure 1B). During training, we progressively increased task difficulty by making stimuli smaller and lower in contrast (see STAR Methods). Increasing task difficulty presented greater opportunity to examine trial-by-trial fluctuations of perception. Mice typically learned this task in 2–3 weeks and performed hundreds of trials per day. Mice performed this detection task using vision. Stimulus location and contrast significantly affected reaction times and detection sensitivity. Mice detected binocular stimuli significantly more rapidly than monocular stimuli, even at lower contrasts (Figure 1C, left versus right panels). Moreover, within a given spatial location, higher contrast stimuli elicited significantly faster reaction times (Figure 1C). Detection sensitivity (d′; see STAR Methods) was significantly greater than chance level in both locations, with binocular vision exhibiting greatest sensitivity (Figure 1D). Lower visual contrast decreased detection sensitivity in both locations, significantly in the binocular visual field (Figure 1D). Sensitivity to stimulus location and contrast did not depend upon grating orientation (not shown). Taken together, these results show that two major aspects of vision—spatial location and contrast—significantly influence visual detection behavior in mice. The remainder of this paper examines the neural correlates of monocular detection in the contralateral hemisphere. Activity in primary visual cortex (V1) was necessary for stimulus detection. Pharmacological or optogenetic inactivation of monocular V1 abolished monocular detection, while interleaved trials of binocular detection were not significantly impaired during the same experiments (Figure S1; see STAR Methods). Inactivation of adjacent non-visual cortex caused no behavioral impairment. These results indicate that localized activity in V1 supports stimulus detection in retinotopically matched regions of visual space. We performed acute recordings of laminar population activity in V1 during visual spatial detection. We recorded in monocular V1 for two reasons. First, monocular detection trials exhibited greatest task difficulty; second, monocular stimuli activate V1 unilaterally, restricting the early stimulus-evoked activity to one hemisphere. We recorded from task-relevant V1 neurons by measuring the spatial receptive field (RF) at each recording site, and ensuring that these overlapped the average location of the monocular stimuli during spatial detection (Figure S2A). LFP was starkly different during successful versus failed detection. Detection failures (Misses) were often accompanied by synchronized, low-frequency (3–7 Hz) LFP oscillations during the stimulus (Figure 2A, gray). By functionally identifying cortical layers (Niell and Stryker, 2008Niell C.M. Stryker M.P. Highly selective receptive fields in mouse visual cortex.J. Neurosci. 2008; 28: 7520-7536Crossref PubMed Scopus (656) Google Scholar, Pluta et al., 2015Pluta S. Naka A. Veit J. Telian G. Yao L. Hakim R. Taylor D. Adesnik H. A direct translaminar inhibitory circuit tunes cortical output.Nat. Neurosci. 2015; 18: 1631-1640Crossref PubMed Scopus (68) Google Scholar), we found that these 3- to 7-Hz oscillations were strongest in L4 and L5/6 (Figures S2E and S2F). Moreover, 3- to 7-Hz residual LFP power was selectively and significantly elevated only during failed detection (Miss) trials (Figures 2B–2D). Successful detection was preceded by elevated narrowband gamma (50–70 Hz) LFP in L4. Narrowband gamma residual power was strongest in L4 (Figure S2E), and in the absence of visual contrast. Remarkably, L4 narrowband gamma power varied with behavioral outcome: it was significantly elevated and then suppressed by the onset of visual contrast selectively on Hit trials (Figures 2E–2G). We next examined laminar activity of single neurons comprising two distinct classes: broad waveform regular spiking (RS) putative excitatory neurons, and narrow waveform fast-spiking (FS) putative inhibitory neurons (Figures 3A and 3B ). Spike widths of FS neurons matched those of parvalbumin (PV) interneurons directly activated by channelrhodopsin (Figures S3A–S3C). This suggests that FS neurons in our experiments are PV interneurons. RS and FS neurons displayed two distinct activations during successful detection. The initial visual response peaked and terminated rapidly, hundreds of milliseconds before the average reaction time (Hit trials; Figure 3A). By aligning to reaction times (first lick) on Hit trials, a second, rapidly rising late-phase response emerged prior to the first lick (Figure 3B); this was not present on Miss trials aligned to stimulus offset (Figure 3A, gray; see Figure S5 for Hit trial alignment to stimulus offset). Late-phase activity on Hit trials was not a movement artifact: firing terminated abruptly upon reward delivery, even though mice continued to lick vigorously during reward consumption. Lower firing rates and reduced correlations preceded successful detection. On a cell-by-cell basis, RS and FS neurons in L2/3 and L5/6 fired significantly less before Hit versus Miss trials (Figures 3B and S3D–S3F). Accordingly, RS neuron pairs in L2/3 and L5/6 were significantly less correlated before Hit trials, as were FS pairs in L5/6 (Figure S3; correlations between Gaussian smoothed spike trains; see STAR Methods). Lower correlations prior to successful detection were not driven by higher arousal; pupil dilates with increasing arousal, yet it was significantly smaller before Hit versus Miss trials (Figure S4). Stimuli evoked higher firing rates on Hit versus Miss trials. On a cell-by-cell basis, stimuli detected on Hit trials evoked significantly more spikes during the early sensory response in RS neurons across all layers, and in FS neurons in L4 and L2/3 (Figure 3C); this trend was even more evident after accounting for pre-stimulus activity (Figure S5). More spikes were also evoked during the late sensory response on Hit trials, particularly for FS neurons (Figure 3D; see also Figure S5). Moreover, stimuli evoked lower noise correlations on Hit trials. RS neuron pairs in all layers displayed significantly lower noise correlations on Hit versus Miss trials, as did FS neuron pairs in L2/3 (Figure S3). These noise correlations were normally distributed across pairs, showing little evidence for discrete clusters of high and low correlation pairs (not shown). Which aspects of cortical states predict trial-by-trial visual spatial detection performance? We observed robust signatures of cortical states across network, laminar, and cellular levels, even when averaging across multiple behavioral sessions and subjects. We thus quantified the accuracy of predicting single-trial behavior from cortical state signatures at both network (LFP) and cellular (RS/FS neuron) levels. Prior to stimulus onset, RS neurons predicted single-trial behavior more accurately than FS neurons. Increased or decreased firing rates and correlations in RS neurons (associated with Hits and Misses) predicted behavioral outcomes significantly better than chance (Figure 4B; 51 ± 4% versus 56 ± 4% correctly predicted single trials; mean ± SD; p < 0.01; for cross-validated SVM classifier, see STAR Methods). Firing rates and correlations of RS neurons predicted behavior better than both factors in FS neurons (see Figure S6 for laminar effects). Pre-stimulus pupil area also predicted behavioral outcomes significantly better than chance (Figure S4D; 60 ± 3%). Upon stimulus onset, L4 LFP oscillations provided the best predictions of single-trial behavior. Stimulus evoked 3- to 7-Hz L4 LFP power predicted trial outcome with 84 ± 11% accuracy, better than any other stimulus-driven factor (Figures 4C and S7). In parallel, stimulus-evoked suppression of L4 narrowband gamma LFP power predicted 71 ± 10% of single trials. A two-factor ANOVA revealed a significant interaction between time period (pre-stimulus and stimulus) and predictions from LFP power (both low-frequency and narrowband gamma power; F = 100.5, p < 10−50, correction for multiple comparisons). At the level of firing rates (aggregated across layers; see STAR Methods), both RS and FS neurons predicted better than chance (RS, 55 ± 4%; FS, 55 ± 4%), at a level comparable to predictions from pre-stimulus rates. However, measuring stimulus-evoked firing rates relative to pre-stimulus baseline (ΔFiring rate) significantly improved predictions (RS, 62 ± 4%; FS, 63 ± 3%; Figure 4D). Behavioral predictions from firing rates were particularly strong for L2/3 FS cells (Figure S6). Remarkably, pairwise noise correlations between RS neurons predicted behavioral outcome significantly better than firing rates (RS × RS, 75 ± 7%; FS × FS, 71 ± 4%; p < 0.001 for RS noise correlations versus RS rates). RS noise correlations were highly predictive across all layers (Figure S6). Predictions from noise correlations were not sensitive to within-trial synchrony and were not entirely explained by different numbers of spikes on Hits versus Misses (Figure S6). Finally, pupil diameter provided markedly inferior predictions of perceptual outcome compared to firing rates, noise correlations, or stimulus-evoked LFP oscillations. Here, we revealed that specific features of cortical state fluctuations across layers of V1 play an important role for visual spatial perception in mice. Cortical states associated with lower noise correlations, suppressed firing, and elevated gamma power before stimulus onset accurately predicted single trials of visual behavior. During the stimulus, the absence of oscillations in L4, higher stimulus-evoked firing, and lower pairwise noise correlations predicted stimulus detection. Our findings identify previously unknown neural correlates of visual perception in mice and quantify their efficacy in predicting single-trial behavior. We revealed that multiple features of cortical states were significantly predictive of visual behavior in mice. Our study used behavioral outcome as criteria to examine features of cortical states before and during stimulus onset; we then tested how accurately these criteria predicted behavior on completely separate trials, across behavioral sessions, and across mice. We found that cortical states with high pre-stimulus firing rates and correlations predicted failures of visual detection. Recent studies of somatosensory detection found that pre-stimulus and early stimulus evoked activity in somatosensory cortex were not predictive of behavioral outcome (Sachidhanandam et al., 2013Sachidhanandam S. Sreenivasan V. Kyriakatos A. Kremer Y. Petersen C.C. Membrane potential correlates of sensory perception in mouse barrel cortex.Nat. Neurosci. 2013; 16: 1671-1677Crossref PubMed Scopus (226) Google Scholar), whereas late period stimulus activity provided better behavioral predictions (Sachidhanandam et al., 2016Sachidhanandam S. Sermet B.S. Petersen C.C.H. Parvalbumin-expressing GABAergic neurons in mouse barrel cortex contribute to gating a goal-directed sensorimotor transformation.Cell Rep. 2016; 15: 700-706Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar). Differences across studies may arise from circuit organization across cortical areas, or from specific behavioral context. In our visual spatial task, monocular detection was most difficult, and this may have accentuated the relationship between cortical states and behavior (Chen et al., 2008Chen Y. Martinez-Conde S. Macknik S.L. Bereshpolova Y. Swadlow H.A. Alonso J.M. Task difficulty modulates the activity of specific neuronal populations in primary visual cortex.Nat. Neurosci. 2008; 11: 974-982Crossref PubMed Scopus (167) Google Scholar, McGinley et al., 2015aMcGinley M.J. David S.V. McCormick D.A. Cortical membrane potential signature of optimal states for sensory signal detection.Neuron. 2015; 87: 179-192Abstract Full Text Full Text PDF PubMed Scopus (354) Google Scholar, Spitzer et al., 1988Spitzer H. Desimone R. Moran J. Increased attention enhances both behavioral and neuronal performance.Science. 1988; 240: 338-340Crossref PubMed Scopus (558) Google Scholar). Compared to prior studies of full-field contrast detection (Histed et al., 2012Histed M.H. Carvalho L.A. Maunsell J.H. Psychophysical measurement of contrast sensitivity in the behaving mouse.J. Neurophysiol. 2012; 107: 758-765Crossref PubMed Scopus (80) Google Scholar), our task used relatively higher contrast stimuli, but these were 10-fold smaller in area (10–15° diameter circle); moreover, these stimuli were only presented to one eye in the trials considered here. Additionally, our study isolated neural correlates of cortical states and effects on perception in stationary conditions, minimizing complicated interactions between arousal, visual motion, locomotion, and motor control (Niell and Stryker, 2010Niell C.M. Stryker M.P. Modulation of visual responses by behavioral state in mouse visual cortex.Neuron. 2010; 65: 472-479Abstract Full Text Full Text PDF PubMed Scopus (808) Google Scholar, Poort et al., 2015Poort J. Khan A.G. Pachitariu M. Nemri A. Orsolic I. Krupic J. Bauza M. Sahani M. Keller G.B. Mrsic-Flogel T.D. Hofer S.B. Learning enhances sensory and multiple non-sensory representations in primary visual cortex.Neuron. 2015; 86: 1478-1490Abstract Full Text Full Text PDF PubMed Scopus (199) Google Scholar, Saleem et al., 2013Saleem A.B. Ayaz A. Jeffery K.J. Harris K.D. Carandini M. Integration of visual motion and locomotion in mouse visual cortex.Nat. Neurosci. 2013; 16: 1864-1869Crossref PubMed Scopus (224) Google Scholar, Vaiceliunaite et al., 2013Vaiceliunaite A. Erisken S. Franzen F. Katzner S. Busse L. Spatial integration in mouse primary visual cortex.J. Neurophysiol. 2013; 110: 964-972Crossref PubMed Scopus (57) Google Scholar, Vinck et al., 2015Vinck M. Batista-Brito R. Knoblich U. Cardin J.A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding.Neuron. 2015; 86: 740-754Abstract Full Text Full Text PDF PubMed Scopus (377) Google Scholar). In our conditions, pupil area generally predicted behavior less accurately than the simultaneously recorded neural signatures of cortical states. Understanding the effects of cortical states on neural activity and perception across modalities, in a variety of behavioral contexts, remains an important topic for future study. Selective coordination of population activity in specific layers of mouse V1 supported perception. First, elevated narrowband gamma power in L4 before stimulus onset was a major factor predicting correct detection, similar to the role of gamma power in visual detection in primates (Fries, 2015Fries P. Rhythms for cognition: communication through coherence.Neuron. 2015; 88: 220-235Abstract Full Text Full Text PDF PubMed Scopus (1197) Google Scholar, Lima et al., 2011Lima B. Singer W. Neuenschwander S. Gamma responses correlate with temporal expectation in monkey primary visual cortex.J. Neurosci. 2011; 31: 15919-15931Crossref PubMed Scopus (61) Google Scholar, Womelsdorf et al., 2006Womelsdorf T. Fries P. Mitra P.P. Desimone R. Gamma-band synchronization in visual cortex predicts speed of change detection.Nature. 2006; 439: 733-736Crossref PubMed Scopus (563) Google Scholar). Second, low levels of pre-stimulus and stimulus-evoked correlations preceded successful stimulus detection (Cohen and Maunsell, 2009Cohen M.R. Maunsell J.H. Attention improves performance primarily by reducing interneuronal correlations.Nat. Neurosci. 2009; 12: 1594-1600Crossref PubMed Scopus (691) Google Scholar, Mitchell et al., 2009Mitchell J.F. Sundberg K.A. Reynolds J.H. Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4.Neuron. 2009; 63: 879-888Abstract Full Text Full Text PDF PubMed Scopus (484) Google Scholar). We observed that low noise correlations (decorrelation) between RS neurons predicted behavioral outcome better than firing rates, similar to studies of visual perception in primates (Cohen and Maunsell, 2009Cohen M.R. Maunsell J.H. Attention improves performance primarily by reducing interneuronal correlations.Nat. Neurosci. 2009; 12: 1594-1600Crossref PubMed Scopus (691) Google Scholar). However, FS neuron activity did not predict behavior better than RS activity, at odds with other findings (Mitchell et al., 2007Mitchell J.F. Sundberg K.A. Reynolds J.H. Differential attention-dependent response modulation across cell classes in macaque visual area V4.Neuron. 2007; 55: 131-141Abstract Full Text Full Text PDF PubMed Scopus (463) Google Scholar, Snyder et al., 2016Snyder A.C. Morais M.J. Smith M.A. Dynamics of excitatory and inhibitory networks are differentially altered by selective attention.J. Neurophysiol. 2016; 116: 1807-1820Crossref PubMed Scopus (26) Google Scholar). This may be due to differences in RS and FS neuron identification and function in higher mammals (Constantinople et al., 2009Constantinople C.M. Disney A.A. Maffie J. Rudy B. Hawken M.J. Quantitative analysis of neurons with Kv3 potassium channel subunits, Kv3.1b and Kv3.2, in macaque primary visual cortex.J. Comp. Neurol. 2009; 516: 291-311Crossref PubMed Scopus (22) Google Scholar, Soares et al., 2017Soares D. Goldrick I. Lemon R.N. Kraskov A. Greensmith L. Kalmar B. Expression of Kv3.1b potassium channel is widespread in macaque motor cortex pyramidal cells: a histological comparison between rat and macaque.J. Comp. Neurol. 2017; 525: 2164-2174Crossref PubMed Scopus (21) Google Scholar, Vigneswaran et al., 2011Vigneswaran G. Kraskov A. Lemon R.N. Large identified pyramidal cells in macaque motor and premotor cortex exhibit “thin spikes”: implications for cell type classification.J. Neurosci. 2011; 31: 14235-14242Crossref PubMed Scopus (113) Google Scholar). In mice, >90% of FS" @default.
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• W2921338417 title "Cortical State Fluctuations across Layers of V1 during Visual Spatial Perception" @default.
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