Arousal and Locomotion Make Distinct Contributions to Cortical Activity Patterns and Visual Encoding
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Abstract
Spontaneous and sensory-evoked cortical activity is highly state-dependent, yet relatively little is known about transitions between distinct waking states. Patterns of activity in mouse V1 differ dramatically between quiescence and locomotion, but this difference could be explained by either motor feedback or a change in arousal levels. We recorded single cells and local field potentials from area V1 in mice head-fixed on a running wheel and monitored pupil diameter to assay arousal. Using naturally occurring and induced state transitions, we dissociated arousal and locomotion effects in V1. Arousal suppressed spontaneous firing and strongly altered the temporal patterning of population activity. Moreover, heightened arousal increased the signal-to-noise ratio of visual responses and reduced noise correlations. In contrast, increased firing in anticipation of and during movement was attributable to locomotion effects. Our findings suggest complementary roles of arousal and locomotion in promoting functional flexibility in cortical circuits.
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Locomotion is a homogeneous behavioral state tightly coupled to arousal.
“This is because locomotion—where place cell activity has been classically characterized—is a homogeneous behavioral state, tightly coupled to arousal32–35.”
From Place and behavioral modulation of hippocampal neurons during immobility (2026) · cites this paper as [34]
In Results ('Dissociating locomotion and arousal'; page not provided), pupil diameter was consistently correlated with locomotion transitions and remained elevated during running, supporting a close association with arousal. The same passage reports elevated arousal without locomotion, and the paper does not establish that locomotion is a homogeneous behavioral state. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe onset of locomotion, as well as vestibular activation, are correlated with pupil dilations and brain state transitions.
“Indeed, the onset of locomotion, as well as vestibular activation, are correlated with pupil dilations and brain state transitions32,34,36.”
From Place and behavioral modulation of hippocampal neurons during immobility (2026) · cites this paper as [34]
The Results sections ‘Dissociating locomotion and arousal’ and ‘LFP modulation by behavioral state transition’ report pupil dilation around locomotion onset and associated changes in cortical activity. The paper does not report vestibular activation or link it to pupil dilation or brain-state transitions; searches for vestibular activation and stimulation found no such passage. Page numbers were unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredDuring running, the behavioral state is more homogeneous and likely corresponds to a high-arousal state compared to immobility, where the internal state is more variable and spontaneously fluctuating.
“These data indicate that during running, the behavioral state is more homogeneous (and likely corresponding to a high-arousal state34–36) compared to immobility, where the internal state is more variable and spontaneously fluctuating.”
From Place and behavioral modulation of hippocampal neurons during immobility (2026) · cites this paper as [34]
Elman RNNs can be viewed as an approximation to continuous time firing rate models of neural circuits.
“We examined Elman RNNs (“vanilla” RNNs), which are perhaps the simplest RNN architecture capable of theoretically representing any nonlinear dynamical system, 52 and which can be viewed as an approximation to continuous time firing rate models of neural circuits.53”
From Remapping in a recurrent neural network model of navigation and context inference (2023) · cites this paper as [53]
Spontaneous and sensory-evoked activity varies continuously and rapidly during wakefulness.
“This work has revealed that spontaneous and sensory-evoked activity varies continuously and rapidly during wakefulness.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Even without movement, increases in pupil diameter are associated with increased cortical activation and suppression of low-frequency rhythms.
“Even in the absence of movement, increases in pupil diameter (dilation) were found to be associated with increases in cortical activation and suppression of low frequency rhythms.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Initiation of locomotion is preceded by a substantial increase in pupil diameter, concurrent with reduced low-frequency cortical LFP synchronization and reduced hippocampal sharp wave and ripple oscillations.
“Interestingly, the initiation of locomotion is preceded by a substantial increase in pupil diameter (Figures 2–4), concurrent with the reduction of low-frequency cortical local field potential synchronization and reduced hippocampal sharp wave and ripple oscillations.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
The transition from sitting quietly to exploratory behaviors such as whisking and locomotion is intertwined with changes in arousal tracked by pupillometry.
“The transition from sitting quietly to exploratory behaviors (whisking and locomotion) is intertwined with changes in arousal tracked by pupillometry.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Low-frequency cortical activity is enhanced during pupillary constriction, especially below a critical pupil diameter.
“Likewise, low-frequency cortical activity is enhanced during pupillary constriction, especially below a critical level of pupil diameter.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
The relationship between pupil diameter and cortical network activity has been observed in visual, somatosensory, and auditory cortical areas.
“has been observed in visual, somatosensory, and auditory cortical areas”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Simultaneous intracellular membrane potential and LFP recordings with pupil diameter in head-fixed mice revealed a marked relationship between pupil size, low-frequency (2-10 Hz) membrane potential/LFP fluctuations, and exploratory behaviors such as whisking and locomotion.
“Recently, intracellular membrane potential (Vm) and local field potential (LFP) recordings from cortical neurons collected simultaneously with pupil diameter in head-fixed, spontaneously locomoting or whisking mice revealed a marked relationship between 1) pupil size, 2) low frequency (2–10 Hz) fluctuations in membrane potential/LFP, and 3) exploratory behaviors (whisking and locomotion)”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Effects of locomotion on spontaneous discharge rates of presumed pyramidal neurons in other layers are more diverse.
“However, the effects of locomotion on the spontaneous discharge rates of presumed pyramidal neurons recorded in other layers is more diverse”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
A gradual decrease in pupil diameter after locomotion is associated with increased prevalence of low-frequency oscillations.
“This gradual decrease in pupil diameter is associated with an increase in the prevalence of low-frequency oscillations”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Eliciting arousal by air puff in a quiescent mouse significantly increases pupil diameter and strongly suppresses slow rhythmic activity.
“eliciting an increase in arousal through the delivery of an air puff to a quiescent mouse results in a significant increase in pupil diameter and strong suppression of slow rhythmic activity”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Applying the same arousing stimulus during locomotion, when average neuronal activity is elevated, does not suppress firing, whereas a transition to locomotion following induced arousal increases neuronal discharge.
“Application of the same arousing stimulus during locomotion, when average neuronal activity is elevated, does not result in a suppression of firing, whereas a transition to locomotion that occurs following induced arousal results in an increase in neuronal discharge”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Air puff-induced arousal elicits an average decrease in discharge rate of many cortical neurons, unlike the increase in neuronal activity prior to movement.
“However, in contrast with the increase in neuronal activity period prior to movement, air puff-induced arousal elicits an average decrease in the discharge rate of many cortical neurons.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Following cessation of locomotion, head-fixed mice typically transition from high to moderate or low arousal over seconds or longer, as measured by pupil diameter.
“Following the cessation of locomotion, head-fixed mice typically exhibit a transition from high arousal to moderate or low arousal, as measured by pupil diameter, over a period of seconds or longer.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Even in the quiescent state without overt movement, pupil dilation is associated with suppression of low-frequency and enhancement of gamma-frequency cortical network activities, occurring as rapid switching or graded continuous change.
“As described above, even in the quiescent state devoid of overt movements, pupil dilation is associated with suppression of low-frequency, and enhancement of gamma-frequency, cortical network activities that can occur as either a rapid switching (Figure 2A) or as a graded and continuous change”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
A subset of neurons shows a strong increase in evoked response with causally induced transition from low to high arousal.
“and a subset of neurons show a strong increase in evoked response with causally (i.e. airpuff) induced transition from low to high arousal”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Behavioral state is an important determinant of both the temporal precision of sensory responses and gamma-band synchronization.
“Behavioral state is an important determinant of both the temporal precision of sensory responses and gamma-band synchronization.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Spontaneous or causally induced increases in arousal are associated with a monotonic increase in gamma-band synchronization in LFP and relative membrane potential power, and in spike-LFP phase locking.
“Spontaneous or causally induced increases in arousal are associated with a monotonic increase in gamma-band synchronization both in terms of local field potential and relative membrane potential power (Lee et al., 2014; Lima et al., 2011; McGinley et al., 2015; Reimer et al., 2014; Vinck et al., 2015), and spike-local field potential phase locking”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
In mouse visual cortex, the signal-to-noise ratio is small at low arousal and increases at high arousal and during locomotion.
“In mouse visual cortex, the S/N is small at low arousal levels, and increases at high arousal levels and during locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Pupil dilations during quiet wakefulness correspond to increased visual responsiveness and enhanced selectivity.
“Pupil dilations during quiet wakefulness also correspond to an increase in visual responsiveness along with an enhancement of selectivity”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Noise correlations within wakefulness are, to a significant degree, explained by fluctuations in arousal as indexed by pupil diameter.
“Recent work also shows that noise correlations within wakefulness are, to a significant degree, explained by fluctuations in arousal as indexed by pupil diameter (Reimer et al., 2014; Vinck et al., 2015).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Pooling different behavioral states together in the analysis of noise correlations introduces shared variability in neuronal responses, over-estimating their magnitude.
“Moreover, pooling different behavioral states together in the analysis of noise correlations introduces shared variability in neuronal responses, over-estimating their magnitude (Vinck et al., 2015).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Within wakefulness, noise correlations are lower during locomotion than quiescence.
“Within wakefulness, noise correlations are lower during locomotion than quiescence (Figure 6B) (Erisken et al., 2014; Vinck et al., 2015).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as Vinck et al., 2015
Arousal and locomotion are associated with reduced and enhanced spontaneous firing rates in mouse primary visual cortex (V1), respectively, indicating that mechanisms by which gain is enhanced during these two behavioural states may differ.
“Arousal and locomotion are associated with reduced and enhanced spontaneous firing rates in the mouse primary visual cortex (V1)33, respectively, indicating that mechanisms by which gain is enhanced during these two behavioural states may differ.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [33]
In visual cortex, the onset of arousal (as measured by pupil diameter) and locomotion increase neuronal gain.
“In the visual cortex, the onset of arousal (as measured by pupil diameter) and locomotion increase neuronal gain32,33,36,37.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [33]
Gain modulation mechanisms are robustly engaged by changes in internal state.
“Gain modulation mechanisms are also robustly engaged by changes in internal state32–35.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [33]
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Cites
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694 · see citing works
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472 · see citing works
Über das Elektrenkephalogramm des Menschen
Berger, H. (1929) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01797193 · Cited by 4,266 · see citing works
Thalamocortical oscillations in the sleeping and aroused brain
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993) · Science · doi:10.1126/science.8235588 · Cited by 3,564 · see citing works
Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention
Fries, P., Reynolds, J. H., Rorie, A. E., & Desimone, R. (2001) · Science · doi:10.1126/science.1055465 · Cited by 2,786 · see citing works
Parvalbumin neurons and gamma rhythms enhance cortical circuit performance
Sohal, V. S., Zhang, F., Yizhar, O., & Deisseroth, K. (2009) · Nature · doi:10.1038/nature07991 · Cited by 2,606 · see citing works
Driving fast-spiking cells induces gamma rhythm and controls sensory responses
Cardin, J. A., Carlén, M., Meletis, K., Knoblich, U., Zhang, F., Deisseroth, K., Tsai, L.-H., & Moore, C. I. (2009) · Nature · doi:10.1038/nature08002 · Cited by 2,603 · see citing works
Self-citationWhittington, M. A., Traub, R. D., & Jefferys, J. G. R. (1995) · Nature · doi:10.1038/373612a0 · Cited by 1,613 · see citing works
Primate frontal eye fields. I. Single neurons discharging before saccades.
Bruce, C. J., & Goldberg, M. E. (1985) · Journal of Neurophysiology · doi:10.1152/jn.1985.53.3.603 · Cited by 1,584 · see citing works
Modulation of visual responses by behavioral state in mouse visual cortex
Niell, C. M., & Stryker, M. P. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.033 · Cited by 1,483 · see citing works
Natural waking and sleep states: a view from inside neocortical neurons
Steriade, M., Timofeev, I., & Grenier, F. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.85.5.1969 · Cited by 1,296 · see citing works
Sleep and arousal: thalamocortical mechanisms
McCormick, D. A., & Bal, T. (1997) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.20.1.185 · Cited by 1,267 · see citing works
Attention improves performance primarily by reducing interneuronal correlations
Cohen, M. R., & Maunsell, J. H. R. (2009) · Nature Neuroscience · doi:10.1038/nn.2439 · Cited by 1,126 · see citing works
Coding of intention in the posterior parietal cortex
Snyder, L. H., Batista, A. P., & Andersen, R. A. (1997) · Nature · doi:10.1038/386167a0 · Cited by 1,103 · see citing works
Nucleus basalis and thalamic control of neocortical activity in the freely moving rat
Buzsáki, G., Bickford, R. G., Ponomareff, G., Thal, L. J., Mandel, R. J., & Gage, F. H. (1988) · Journal of Neuroscience · doi:10.1523/jneurosci.08-11-04007.1988 · Cited by 1,039 · see citing works
Measuring and interpreting neuronal correlations
Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,016 · see citing works
Tuning arousal with optogenetic modulation of locus coeruleus neurons
Carter, M. E., Yizhar, O., Chikahisa, S., Nguyen, H., Adamantidis, A., Nishino, S., Deisseroth, K., & de Lecea, L. (2010) · Nature Neuroscience · doi:10.1038/nn.2682 · Cited by 993 · see citing works
Mechanisms of Gamma Oscillations in the Hippocampus of the Behaving Rat
Csicsvari, J., Jamieson, B. G., Wise, K. D., & Buzsáki, G. (2003) · Neuron · doi:10.1016/s0896-6273(02)01169-8 · Cited by 990 · see citing works
A Cortical Circuit for Gain Control by Behavioral State
Fu, Y., Tucciarone, J. M., Espinosa, J. S., Sheng, N., Darcy, D. P., Nicoll, R. A., Huang, Z. J., & Stryker, M. P. (2014) · Cell · doi:10.1016/j.cell.2014.01.050 · Cited by 937 · see citing works
van Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., van der Togt, C., & Roelfsema, P. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402773111 · Cited by 918 · see citing works
Harris, K. D., & Thiele, A. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn3084 · Cited by 871 · see citing works
Long-range and local circuits for top-down modulation of visual cortex processing
Zhang, S., Xu, M., Kamigaki, T., Hoang Do, J. P., Chang, W.-C., Jenvay, S., Miyamichi, K., Luo, L., & Dan, Y. (2014) · Science · doi:10.1126/science.1254126 · Cited by 804 · see citing works
Gilzenrat, M. S., Nieuwenhuis, S., Jepma, M., & Cohen, J. D. (2010) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.10.2.252 · Cited by 798 · see citing works
A disinhibitory circuit mediates motor integration in the somatosensory cortex
Lee, S., Kruglikov, I., Huang, Z. J., Fishell, G., & Rudy, B. (2013) · Nature Neuroscience · doi:10.1038/nn.3544 · Cited by 758 · see citing works
Information processing with population codes
Pouget, A., Dayan, P., & Zemel, R. S. (2000) · Nature reviews. Neuroscience · doi:10.1038/35039062 · Cited by 744 · see citing works
Pupil fluctuations track fast switching of cortical states during quiet wakefulness
Reimer, J., Froudarakis, E., Cadwell, C. R., Yatsenko, D., Denfield, G. H., & Tolias, A. S. (2014) · Neuron · doi:10.1016/j.neuron.2014.09.033 · Cited by 717 · see citing works
Flow of cortical activity underlying a tactile decision in mice
Guo, Z. V., Li, N., Huber, D., Ophir, E., Gutnisky, D., Ting, J. T., Feng, G., & Svoboda, K. (2014) · Neuron · doi:10.1016/j.neuron.2013.10.020 · Cited by 715 · see citing works
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.013 · Cited by 710 · see citing works
Sensorimotor mismatch signals in primary visual cortex of the behaving mouse
Keller, G. B., Bonhoeffer, T., & Hübener, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.040 · Cited by 668 · see citing works
Quantitative measures of cluster quality for use in extracellular recordings
Schmitzer-Torbert, N. C., Jackson, J. C., Henze, D. A., Harris, K., & Redish, A. D. (2005) · Neuroscience · doi:10.1016/j.neuroscience.2004.09.066 · Cited by 642 · see citing works
Decorrelated neuronal firing in cortical microcircuits
Ecker, A. S., Berens, P., Keliris, G. A., Bethge, M., Logothetis, N. K., & Tolias, A. S. (2010) · Science · doi:10.1126/science.1179867 · Cited by 640 · see citing works
Cellular mechanisms of brain state-dependent gain modulation in visual cortex
Polack, P.-O., Friedman, J., & Golshani, P. (2013) · Nature Neuroscience · doi:10.1038/nn.3464 · Cited by 628 · see citing works
Effects of sleep and arousal on the processing of visual information in the cat
Livingstone, M. S., & Hubel, D. H. (1981) · Nature · doi:10.1038/291554a0 · Cited by 583 · see citing works
A synaptic and circuit basis for corollary discharge in the auditory cortex
Schneider, D. M., Nelson, A., & Mooney, R. (2014) · Nature · doi:10.1038/nature13724 · Cited by 580 · see citing works
Metherate, R., Cox, C. L., & Ashe, J. H. (1992) · Journal of Neuroscience · doi:10.1523/jneurosci.12-12-04701.1992 · Cited by 575 · see citing works
Destexhe, A., Contreras, D., & Steriade, M. (1999) · The Journal of Neuroscience · doi:10.1523/jneurosci.19-11-04595.1999 · Cited by 573 · see citing works
Correlating whisker behavior with membrane potential in barrel cortex of awake mice
Crochet, S., & Petersen, C. (2006) · Nature Neuroscience · doi:10.1038/nn1690 · Cited by 556 · see citing works
Inhibition dominates sensory responses in the awake cortex
Haider, B., Häusser, M., & Carandini, M. (2012) · Nature · doi:10.1038/nature11665 · Cited by 541 · see citing works
Fast modulation of visual perception by basal forebrain cholinergic neurons
Pinto, L., Goard, M. J., Estandian, D., Xu, M., Kwan, A. C., Lee, S.-H., Harrison, T. C., Feng, G., & Dan, Y. (2013) · Nature Neuroscience · doi:10.1038/nn.3552 · Cited by 529 · see citing works
Role of Reticular Activation in the Modulation of Intracortical Synchronization
Munk, M. H. J., Roelfsema, P. R., Peter König, B., Engel, A. K., & Singer, W. (1996) · Science · doi:10.1126/science.272.5259.271 · Cited by 492 · see citing works
Unique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex
Gentet, L. J., Kremer, Y., Taniguchi, H., Huang, Z. J., Staiger, J. F., & Petersen, C. C. H. (2012) · Nature Neuroscience · doi:10.1038/nn.3051 · Cited by 438 · see citing works
Membrane potential dynamics of GABAergic neurons in the barrel cortex of behaving mice
Gentet, L. J., Avermann, M., Matyas, F., Staiger, J. F., & Petersen, C. C. H. (2010) · Neuron · doi:10.1016/j.neuron.2010.01.006 · Cited by 429 · see citing works
Integration of visual motion and locomotion in mouse visual cortex
Saleem, A. B., Ayaz, A., Jeffery, K. J., Harris, K. D., & Carandini, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3567 · Cited by 402 · see citing works
The effects of neural gain on attention and learning
Eldar, E., Cohen, J. D., & Niv, Y. (2013) · Nature Neuroscience · doi:10.1038/nn.3428 · Cited by 402 · see citing works
Effects and Mechanisms of Wakefulness on Local Cortical Networks
Constantinople, C. M., & Bruno, R. M. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.040 · Cited by 387 · see citing works
Contreras, D., Timofeev, I., & Steriade, M. (1996) · The Journal of Physiology · doi:10.1113/jphysiol.1996.sp021488 · Cited by 374 · see citing works
Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex
Zhou, M., Liang, F., Xiong, X. R., Li, L., Li, H., Xiao, Z., Tao, H. W., & Zhang, L. I. (2014) · Nature Neuroscience · doi:10.1038/nn.3701 · Cited by 316 · see citing works
Direct connections of rat visual cortex with sensory, motor, and association cortices
Miller, M. W., & Vogt, B. A. (1984) · The Journal of Comparative Neurology · doi:10.1002/cne.902260204 · Cited by 300 · see citing works
High-Dimensional Cluster Analysis with the Masked EM Algorithm
Kadir, S., Goodman, D. F. M., & Harris, K. D. (2014) · Neural Computation · doi:10.1162/neco_a_00661 · Cited by 295 · see citing works
Subthreshold mechanisms underlying state-dependent modulation of visual responses
Bennett, C., Arroyo, S., & Hestrin, S. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.007 · Cited by 292 · see citing works
The Detection of Visual Contrast in the Behaving Mouse
Busse, L., Ayaz, A., Dhruv, N. T., Katzner, S., Saleem, A. B., Schölvinck, M. L., Zaharia, A. D., & Carandini, M. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6689-10.2011 · Cited by 286 · see citing works
Effects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex
McAdams, C. J., & Maunsell, J. H. R. (1999) · Neuron · doi:10.1016/s0896-6273(01)80034-9 · Cited by 286 · see citing works
Identification of a brainstem circuit regulating visual cortical state in parallel with locomotion
Lee, A. M., Hoy, J. L., Bonci, A., Wilbrecht, L., Stryker, M. P., & Niell, C. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.06.031 · Cited by 282 · see citing works
Motor cortex feedback influences sensory processing by modulating network state
Zagha, E., Casale, A. E., Sachdev, R. N. S., McGinley, M. J., & McCormick, D. A. (2013) · Neuron · doi:10.1016/j.neuron.2013.06.008 · Cited by 268 · see citing works
Relating neuronal firing patterns to functional differentiation of cerebral cortex.
Shinomoto, S., Kim, H., Shimokawa, T., Matsuno, N., Funahashi, S., Shima, K., Fujita, I., Tamura, H., Doi, T., Kawano, K., et al. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000433 · Cited by 239 · see citing works
Gateways of Ventral and Dorsal Streams in Mouse Visual Cortex
Wang, Q., Gao, E., & Burkhalter, A. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.3488-10.2011 · Cited by 232 · see citing works
Attentional Modulation of Cell-Class-Specific Gamma-Band Synchronization in Awake Monkey Area V4
Vinck, M. A., Womelsdorf, T., Buffalo, E. A., Desimone, R., & Fries, P. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.019 · Cited by 205 · see citing works
Self-citationState changes rapidly modulate cortical neuronal responsiveness
Hasenstaub, A., Sachdev, R. N. S., & McCormick, D. A. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.2184-07.2007 · Cited by 196 · see citing works
Herrero, J. L., Gieselmann, M. A., Sanayei, M., & Thiele, A. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.029 · Cited by 151 · see citing works
Improved measures of phase-coupling between spikes and the Local Field Potential
Vinck, M. A., Battaglia, F. P., Womelsdorf, T., & Pennartz, C. M. A. (2011) · Journal of Computational Neuroscience · doi:10.1007/s10827-011-0374-4 · Cited by 145 · see citing works
Self-citationWomelsdorf, T., Lima, B., Vinck, M., Oostenveld, R., Singer, W., Neuenschwander, S., & Fries, P. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1114223109 · Cited by 119 · see citing works
Self-citationÜber das Elektrenkephalogramm des Menschen
Berger, H. (1934) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01789984 · Cited by 92 · see citing works
Spatiotemporal properties of sensory responses in vivo are strongly dependent on network context
Civillico, E. F., & Contreras, D. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00025 · Cited by 56 · see citing works
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Spontaneous behaviors drive multidimensional, brainwide activity
Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019) · Science · doi:10.1126/science.aav7893 · Cited by 1,510 · see citing works
Joshi, S., Li, Y., Kalwani, R. M., & Gold, J. I. (2016) · Neuron · doi:10.1016/j.neuron.2015.11.028 · Cited by 1,387 · see citing works
Single-trial neural dynamics are dominated by richly varied movements
Musall, S., Kaufman, M. T., Juavinett, A., Gluf, S., & Churchland, A. K. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0502-4 · Cited by 1,057 · see citing works
Waking state: rapid variations modulate neural and behavioral responses
McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.012 · Cited by 801 · see citing works
Self-citationPupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex
Reimer, J., McGinley, M. J., Liu, Y., Rodenkirch, C., Wang, Q., McCormick, D. A., & Tolias, A. S. (2016) · Nature Communications · doi:10.1038/ncomms13289 · Cited by 926 · see citing works
Predictive Processing: A Canonical Cortical Computation
Keller, G. B., & Mrsic-Flogel, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.003 · Cited by 836 · see citing works
Distributed coding of choice, action and engagement across the mouse brain
Steinmetz, N. A., Zatka-Haas, P., Carandini, M., & Harris, K. D. (2019) · Nature · doi:10.1038/s41586-019-1787-x · Cited by 759 · see citing works
Cortical membrane potential signature of optimal states for sensory signal detection
McGinley, M. J., David, S. V., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.05.038 · Cited by 728 · see citing works
Mather, M., Clewett, D., Sakaki, M., & Harley, C. W. (2016) · Behavioral and Brain Sciences · doi:10.1017/s0140525x15000667 · Cited by 695 · see citing works
Pupil Size as a Window on Neural Substrates of Cognition
Joshi, S., & Gold, J. I. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.03.005 · Cited by 620 · see citing works
Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias
Urai, A. E., Braun, A., & Donner, T. H. (2017) · Nature Communications · doi:10.1038/ncomms14637 · Cited by 466 · see citing works
Cortical gamma band synchronization through somatostatin interneurons
Veit, J., Hakim, R., Jadi, M. P., B, T. J. S. A., & Adesnik, H. (2017) · Nature Neuroscience · doi:10.1038/nn.4562 · Cited by 364 · see citing works
Pesaran, B., Vinck, M. A., Einevoll, G. T., Sirota, A., Fries, P., Siegel, M., Truccolo, W., Schroeder, C. E., & Srinivasan, R. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0171-8 · Cited by 365 · see citing works
Self-citationActive control of arousal by a locus coeruleus GABAergic circuit
Breton‐Provencher, V., & Sur, M. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0305-z · Cited by 348 · see citing works
Neuromodulation of brain state and behavior
McCormick, D. A., Nestvogel, D. B., & He, B. J. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100219-105424 · Cited by 303 · see citing works
Large-scale neural recordings call for new insights to link brain and behavior
Urai, A. E., Doiron, B., Leifer, A. M., & Churchland, A. K. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00980-9 · Cited by 324 · see citing works
Mechanisms underlying gain modulation in the cortex
Ferguson, K. A., & Cardin, J. A. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0253-y · Cited by 327 · see citing works
Self-citationDynamic modulation of decision biases by brainstem arousal systems
de Gee, J. W., Colizoli, O., Kloosterman, N. A., Knapen, T. H. J., Nieuwenhuis, S., & Donner, T. H. (2017) · eLife · doi:10.7554/elife.23232 · Cited by 312 · see citing works
Dorsal Raphe Dopamine Neurons Modulate Arousal and Promote Wakefulness by Salient Stimuli
Cho, J. R., Treweek, J. B., Robinson, J. E., Xiao, C., Bremner, L. R., Greenbaum, A., & Gradinaru, V. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.020 · Cited by 276 · see citing works
Vision and Locomotion Shape the Interactions between Neuron Types in Mouse Visual Cortex
Dipoppa, M., Ranson, A., Krumin, M., Pachitariu, M., Carandini, M., & Harris, K. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.037 · Cited by 280 · see citing works
Video-rate volumetric functional imaging of the brain at synaptic resolution
Lu, R., Sun, W., Liang, Y., Kerlin, A., Bierfeld, J., Seelig, J. D., Wilson, D. E., Scholl, B., Mohar, B., Tanimoto, M., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4516 · Cited by 272 · see citing works
Sensorimotor processing in the rodent barrel cortex
Petersen, C. C. H. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0200-y · Cited by 271 · see citing works
Neuromodulatory Correlates of Pupil Dilation
Larsen, R. S., & Waters, J. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00021 · Cited by 263 · see citing works
Mice alternate between discrete strategies during perceptual decision-making
Ashwood, Z. C., Roy, N. A., Stone, I. R., Laboratory, T. I. B., Urai, A. E., Churchland, A. K., Pouget, A., & Pillow, J. W. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-01007-z · Cited by 229 · see citing works
Locus coeruleus norepinephrine activity mediates sensory-evoked awakenings from sleep
Hayat, H., Regev, N., Matosevich, N., Sales, A. C., Paredes-Rodriguez, E., Krom, A. J., Bergman, L., Li, Y., Lavigne, M., Kremer, E. J., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz4232 · Cited by 255 · see citing works
The population doctrine in cognitive neuroscience
Ebitz, R. B., & Hayden, B. Y. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.011 · Cited by 273 · see citing works
The mechanics of state-dependent neural correlations
Doiron, B., Litwin-Kumar, A., Rosenbaum, R., Ocker, G. K., & Josić́, K. (2016) · Nature Neuroscience · doi:10.1038/nn.4242 · Cited by 236 · see citing works
Representational drift in the mouse visual cortex
Deitch, D., Rubin, A., & Ziv, Y. (2021) · Current Biology · doi:10.1016/j.cub.2021.07.062 · Cited by 227 · see citing works
Liu, Y., Rodenkirch, C., Moskowitz, N., Schriver, B. J., & Wang, Q. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.08.094 · Cited by 212 · see citing works
Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1
Dadarlat, M. C., & Stryker, M. P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.2728-16.2017 · Cited by 205 · see citing works
Recent advances in neurotechnologies with broad potential for neuroscience research
Vázquez-Guardado, A., Yang, Y., Bandodkar, A. J., & Rogers, J. A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00739-8 · Cited by 202 · see citing works
Breathing coordinates cortico-hippocampal dynamics in mice during offline states
Karalis, N., & Sirota, A. (2022) · Nature Communications · doi:10.1038/s41467-022-28090-5 · Cited by 179 · see citing works
The mouse prefrontal cortex: Unity in diversity
Le Merre, P., Ährlund-Richter, S., & Carlén, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.035 · Cited by 207 · see citing works
Altered Cortical Ensembles in Mouse Models of Schizophrenia
Hamm, J. P., Peterka, D. S., Gogos, J. A., & Yuste, R. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.019 · Cited by 175 · see citing works
Selective modulation of cortical state during spatial attention
Engel, T. A., Steinmetz, N. A., Gieselmann, M. A., Thiele, A., Moore, T., & Boahen, K. A. (2016) · Science · doi:10.1126/science.aag1420 · Cited by 207 · see citing works
Cooperative subnetworks of molecularly similar interneurons in mouse neocortex
Karnani, M. M., Jackson, J., Ayzenshtat, I., Tucciarone, J., Manoocheri, K., Snider, W. G., & Yuste, R. (2016) · Neuron · doi:10.1016/j.neuron.2016.02.037 · Cited by 196 · see citing works
A Motor Theory of Sleep-Wake Control: Arousal-Action Circuit
Liu, D., & Dan, Y. (2019) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061813 · Cited by 190 · see citing works
Nelson, A., & MOONEY, R. D. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.031 · Cited by 190 · see citing works
Unexpected arousal modulates the influence of sensory noise on confidence
Allen, M., Frank, D., Schwarzkopf, D. S., Fardo, F., Winston, J. S., Hauser, T. U., & Rees, G. (2016) · eLife · doi:10.7554/elife.18103 · Cited by 182 · see citing works
A Disinhibitory Circuit for Contextual Modulation in Primary Visual Cortex
Keller, A. J., Dipoppa, M., Roth, M., Caudill, M. S., Ingrosso, A., Miller, K. D., & Scanziani, M. (2020) · Neuron · doi:10.1016/j.neuron.2020.11.013 · Cited by 177 · see citing works
Natural behavior is the language of the brain
Miller, C. T., Gire, D. H., Hoke, K. L., Huk, A. C., Kelley, D. B., Leopold, D. A., Smear, M. C., Theunissen, F. E., Yartsev, M. M., & Niell, C. M. (2022) · Current Biology · doi:10.1016/j.cub.2022.03.031 · Cited by 170 · see citing works
A midline thalamic circuit determines reactions to visual threat
Salay, L. D., Ishiko, N., & Huberman, A. D. (2018) · Nature · doi:10.1038/s41586-018-0078-2 · Cited by 164 · see citing works
Stimulus-dependent representational drift in primary visual cortex
Marks, T. D., & Goard, M. J. (2021) · Nature Communications · doi:10.1038/s41467-021-25436-3 · Cited by 175 · see citing works
Qiang, Y., Artoni, P., Seo, K. J., Culaclii, S., Hogan, V., Zhao, X., Zhong, Y., Han, X., Wang, P., Lo, Y., et al. (2018) · Science Advances · doi:10.1126/sciadv.aat0626 · Cited by 153 · see citing works
Arousal Modulates Retinal Output
Schröder, S., Steinmetz, N. A., Krumin, M., Pachitariu, M., Rizzi, M., Lagnado, L., Harris, K. D., & Carandini, M. (2020) · Neuron · doi:10.1016/j.neuron.2020.04.026 · Cited by 156 · see citing works
Over and above frequency: Gamma oscillations as units of neural circuit operations
Fernández‐Ruiz, A., Sirota, A., Lopes‐dos‐Santos, V., & Dupret, D. (2023) · Neuron · doi:10.1016/j.neuron.2023.02.026 · Cited by 167 · see citing works
Movement-Related Signals in Sensory Areas: Roles in Natural Behavior
Parker, P. R. L., Brown, M. A., Smear, M. C., & Niell, C. M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.05.005 · Cited by 149 · see citing works
Quantitative Predictions Orchestrate Visual Signaling in Drosophila
Kim, A. J., Fenk, L. M., Lyu, C., & Maimon, G. (2017) · Cell · doi:10.1016/j.cell.2016.12.005 · Cited by 139 · see citing works
Subcortical Source and Modulation of the Narrowband Gamma Oscillation in Mouse Visual Cortex
Saleem, A. B., Lien, A. D., Krumin, M., Haider, B., Rosón, M. R., Ayaz, A., Reinhold, K., Busse, L., Carandini, M., & Harris, K. D. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.028 · Cited by 153 · see citing works
Yüzgeç, Ö., Prsa, M., Zimmermann, R. A., & Huber, D. (2018) · Current Biology · doi:10.1016/j.cub.2017.12.049 · Cited by 153 · see citing works
Why is everyone talking about brain state?
Greene, A. S., Horien, C. L., Barson, D. G., Scheinost, D., & Constable, R. T. (2023) · Trends in Neurosciences · doi:10.1016/j.tins.2023.04.001 · Cited by 148 · see citing works
Vagus nerve stimulation induces widespread cortical and behavioral activation
Collins, L. N., Boddington, L. J., Steffan, P. J., & McCormick, D. A. (2021) · Current Biology · doi:10.1016/j.cub.2021.02.049 · Cited by 147 · see citing works
Mesoscopic Imaging: Shining a Wide Light on Large-Scale Neural Dynamics
Cardin, J. A., Crair, M. C., & Higley, M. J. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.031 · Cited by 135 · see citing works
Self-citationBrain activity mapping at multiple scales with silicon microprobes containing 1,024 electrodes
Shobe, J. L., Claar, L. D., Parhami, S., Bakhurin, K. I., & Masmanidis, S. C. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00464.2015 · Cited by 146 · see citing works
Spatiotemporally heterogeneous coordination of cholinergic and neocortical activity
Lohani, S., Moberly, A. H., Benisty, H., Landa, B., Jing, M., Li, Y., Higley, M. J., & Cardin, J. A. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01202-6 · Cited by 146 · see citing works
Self-citationMembrane Potential Dynamics of Spontaneous and Visually Evoked Gamma Activity in V1 of Awake Mice
Perrenoud, Q., Pennartz, C. M. A., & Gentet, L. J. (2016) · PLoS Biology · doi:10.1371/journal.pbio.1002383 · Cited by 136 · see citing works
Locus coeruleus-norepinephrine modulation of sensory processing and perception: A focused review
McBurney-Lin, J., Lu, J., Zuo, Y., & Yang, H. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.06.009 · Cited by 137 · see citing works
Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits
Barson, D. G., Hamodi, A. S., Shen, X., Lür, G., Constable, R. T., Cardin, J. A., Crair, M. C., & Higley, M. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0625-2 · Cited by 140 · see citing works
Self-citationDevelopmental Dysfunction of VIP Interneurons Impairs Cortical Circuits
Batista-Brito, R., Vinck, M., Ferguson, K. A., Chang, J. T., Laubender, D., Lur, G., Mossner, J. M., Hernandez, V. G., Ramakrishnan, C., Deisseroth, K., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.034 · Cited by 135 · see citing works
Self-citationWhat is the dynamical regime of cerebral cortex?
Ahmadian, Y., & Miller, K. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.031 · Cited by 140 · see citing works
Locomotion-dependent remapping of distributed cortical networks
Clancy, K. B., Oršolić, I., & Mrsic‐Flogel, T. D. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0357-8 · Cited by 137 · see citing works
Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex
Cowley, B. R., Snyder, A. C., Acar, K., Williamson, R. C., Yu, B. M., & Smith, M. A. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.021 · Cited by 127 · see citing works
Phasic Activation of Dorsal Raphe Serotonergic Neurons Increases Pupil Size
Cazettes, F., Reato, D., Morais, J. P., Renart, A., & Mainen, Z. F. (2020) · Current Biology · doi:10.1016/j.cub.2020.09.090 · Cited by 133 · see citing works
Facemap: a framework for modeling neural activity based on orofacial tracking
Syeda, A., Zhong, L., Tung, R., Long, W., Pachitariu, M., & Stringer, C. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01490-6 · Cited by 133 · see citing works
Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains
de Gee, J. W., Tsetsos, K., Schwabe, L., Urai, A. E., McCormick, D. A., McGinley, M. J., & Donner, T. H. (2020) · eLife · doi:10.7554/elife.54014 · Cited by 125 · see citing works
Synaptic Mechanisms of Feature Coding in the Visual Cortex of Awake Mice
Adesnik, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.08.014 · Cited by 130 · see citing works
Norepinephrine links astrocytic activity to regulation of cortical state
Reitman, M. E., Tse, V., Mi, X., Willoughby, D. D., Peinado, A., Aivazidis, A., Myagmar, B., Simpson, P. C., Bayraktar, O. A., Yu, G., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01284-w · Cited by 128 · see citing works
Speed dependent descending control of freezing behavior in Drosophila melanogaster
Zacarias, R., Namiki, S., Card, G. M., Vasconcelos, M. L., & Moita, M. A. (2018) · Nature Communications · doi:10.1038/s41467-018-05875-1 · Cited by 106 · see citing works
Montijn, J. S., Meijer, G. T., Lansink, C. S., & Pennartz, C. M. A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.07.065 · Cited by 124 · see citing works
Walking enhances peripheral visual processing in humans
Cao, L., & Händel, B. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000511 · Cited by 121 · see citing works
Vassalli, A., & Franken, P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1700983114 · Cited by 117 · see citing works
Rodenkirch, C., Liu, Y., Schriver, B. J., & Wang, Q. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0283-1 · Cited by 118 · see citing works
The Cortical States of Wakefulness
Poulet, J. F. A., & Crochet, S. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00064 · Cited by 119 · see citing works
Reward Association Enhances Stimulus-Specific Representations in Primary Visual Cortex
Henschke, J. U., Dylda, E., Katsanevaki, D., Dupuy, N., Currie, S. P., Amvrosiadis, T., Pakan, J. M. P., & Rochefort, N. L. (2020) · Current Biology · doi:10.1016/j.cub.2020.03.018 · Cited by 116 · see citing works
Behavioral origin of sound-evoked activity in mouse visual cortex
Bimbard, C., Sit, T. P. H., Lebedeva, A., Reddy, C. B., Harris, K. D., & Carandini, M. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01227-x · Cited by 112 · see citing works
Interstitial ions: A key regulator of state-dependent neural activity?
Rasmussen, R. N., O’Donnell, J., Ding, F., & Nedergaard, M. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101802 · Cited by 113 · see citing works
Graded recruitment of pupil-linked neuromodulation by parametric stimulation of the vagus nerve
Mridha, Z., de Gee, J. W., Shi, Y., Alkashgari, R., Williams, J. C., Suminski, A. J., Ward, M. P., Zhang, W., & McGinley, M. J. (2021) · Nature Communications · doi:10.1038/s41467-021-21730-2 · Cited by 114 · see citing works
Real-world stress resilience is associated with the responsivity of the locus coeruleus
Grueschow, M., Stenz, N., Thörn, H., Ehlert, U., Breckwoldt, J., Brodmann Maeder, M., Exadaktylos, A. K., Bingisser, R., Ruff, C. C., & Kleim, B. (2021) · Nature Communications · doi:10.1038/s41467-021-22509-1 · Cited by 116 · see citing works
Cracking the Function of Layers in the Sensory Cortex
Adesnik, H., & Naka, A. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.032 · Cited by 111 · see citing works
Respiratory rhythms of the predictive mind.
Allen, M., Varga, S., & Heck, D. H. (2023) · Psychological Review · doi:10.1037/rev0000391 · Cited by 114 · see citing works
Spontaneous behaviors drive multidimensional, brain-wide activityPreprint
Stringer, C., Pachitariu, M., Steinmetz, N. A., Reddy, C. B., Carandini, M., & Harris, K. D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/306019 · Cited by 119 · see citing works
Cerebral oxygenation during locomotion is modulated by respiration
Zhang, Q., Roche, M., Gheres, K. W., Chaigneau, E. M., Kedarasetti, R. T., Haselden, W. D., Charpak, S., & Drew, P. J. (2019) · Nature Communications · doi:10.1038/s41467-019-13523-5 · Cited by 121 · see citing works
Bottom-up and top-down modulation of multisensory integration
Choi, I., Lee, J. Y., & Lee, S. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.05.002 · Cited by 110 · see citing works
How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model
Niell, C. M., & Scanziani, M. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-102320-085825 · Cited by 109 · see citing works
Pupil diameter is not an accurate real-time readout of locus coeruleus activity
Megemont, M., McBurney-Lin, J., & Yang, H. (2022) · eLife · doi:10.7554/elife.70510 · Cited by 115 · see citing works
Pfeffer, T. A., Keitel, C., Kluger, D. S., Keitel, A., Russmann, A., Thut, G., Donner, T. H., & Groß, J. (2022) · eLife · doi:10.7554/elife.71890 · Cited by 112 · see citing works
Waschke, L., Tune, S., & Obleser, J. (2019) · eLife · doi:10.7554/elife.51501 · Cited by 105 · see citing works
Effects of Arousal on Mouse Sensory Cortex Depend on Modality
Shimaoka, D., Harris, K. D., & Carandini, M. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.02.092 · Cited by 105 · see citing works
Inhibitory control of correlated intrinsic variability in cortical networks
Stringer, C., Pachitariu, M., Steinmetz, N. A., Okun, M., Barthó, P., Harris, K. D., Sahani, M., & Lesica, N. A. (2016) · eLife · doi:10.7554/elife.19695 · Cited by 100 · see citing works
Thalamocortical excitability modulation guides human perception under uncertainty
Kosciessa, J. Q., Lindenberger, U., & Garrett, D. D. (2021) · Nature Communications · doi:10.1038/s41467-021-22511-7 · Cited by 101 · see citing works
Statistical learning attenuates visual activity only for attended stimuli
Richter, D., & de Lange, F. P. (2019) · eLife · doi:10.7554/elife.47869 · Cited by 102 · see citing works
Locomotor activity modulates associative learning in mouse cerebellum
Albergaria, C., Silva, N. T., Pritchett, D. L., & Carey, M. R. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0129-x · Cited by 99 · see citing works
Dual-polarity voltage imaging of the concurrent dynamics of multiple neuron types
Kannan, M., Vasan, G., Haziza, S., Huang, C., Chrapkiewicz, R., Luo, J., Cardin, J. A., Schnitzer, M. J., & Pieribone, V. A. (2022) · Science · doi:10.1126/science.abm8797 · Cited by 92 · see citing works
Self-citationDynamics of auditory cortical activity during behavioural engagement and auditory perception
Carcea, I., Insanally, M. N., & Froemke, R. C. (2017) · Nature Communications · doi:10.1038/ncomms14412 · Cited by 96 · see citing works
van Kempen, J., Loughnane, G. M., Newman, D. P., Kelly, S. P., Thiele, A., O’Connell, R. G., & Bellgrove, M. A. (2019) · eLife · doi:10.7554/elife.42541 · Cited by 88 · see citing works
VIP+ interneurons control neocortical activity across brain states
Jackson, J., Ayzenshtat, I., Karnani, M., & Yuste, R. (2016) · Journal of Neurophysiology · doi:10.1152/jn.01124.2015 · Cited by 93 · see citing works
Dedicated Hippocampal Inhibitory Networks for Locomotion and Immobility
Arriaga, M., & Han, E. B. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1076-17.2017 · Cited by 95 · see citing works
Malina, K. C.-K., Tsivourakis, E., Kushinsky, D., Apelblat, D., Shtiglitz, S., Zohar, E., Sokoletsky, M., Tasaka, G., Mizrahi, A., Lampl, I., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.05.001 · Cited by 99 · see citing works
Snapshots of the Brain in Action: Local Circuit Operations through the Lens of γ Oscillations
Cardin, J. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1021-16.2016 · Cited by 89 · see citing works
Self-citationAttention model of binocular rivalry
Li, H.-H., Rankin, J., Rinzel, J., Carrasco, M., & Heeger, D. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1620475114 · Cited by 85 · see citing works
- Authors
- Vinck, M., Batista-Brito, R., Knoblich, U., & Cardin, J. A.
- Affiliations
- Yale University (US)
- Corresponding author
- Cardin, Jessica A.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 86
- Issue
- 3
- Pages
- 740-754
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC4425590
- OpenAlex
- W2086338669
- Cited by
- 819
- References
- 63
- Citations by year
- 2026: 71 · 2025: 78 · 2024: 108 · 2023: 92 · 2022: 89 · 2021: 76 · 2020: 71 · 2019: 72 · 2018: 57 · 2017: 52 · 2016: 41 · 2015: 12
- FWCI
- 32.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- McKnight Foundation
- National Alliance for Research on Schizophrenia and Depression
- National Institutes of Health
- National Eye Institute (EY022951, R00EY018407, R01EY022951, P30EY026878)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
- Keywords
- arousal, locomotion, primary visual cortex, spontaneous cortical activity, population activity, signal-to-noise ratio, noise correlation, pupil size, running wheel, motor feedback, visual encoding, mice
- MeSH terms
- Action Potentials, Animals, Arousal, Computer Simulation, Fourier Analysis, Locomotion, Male, Pupil, Visual Cortex, Visual Pathways, Wakefulness, Signal Detection, Psychological, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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