The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Cortical Membrane Potential Signature of Optimal States for Sensory Signal Detection
Open accessAbstract
The neural correlates of optimal states for signal detection task performance are largely unknown. One hypothesis holds that optimal states exhibit tonically depolarized cortical neurons with enhanced spiking activity, such as occur during movement. We recorded membrane potentials of auditory cortical neurons in mice trained on a challenging tone-in-noise detection task while assessing arousal with simultaneous pupillometry and hippocampal recordings. Arousal measures accurately predicted multiple modes of membrane potential activity, including rhythmic slow oscillations at low arousal, stable hyperpolarization at intermediate arousal, and depolarization during phasic or tonic periods of hyper-arousal. Walking always occurred during hyper-arousal. Optimal signal detection behavior and sound-evoked responses, at both sub-threshold and spiking levels, occurred at intermediate arousal when pre-decision membrane potentials were stably hyperpolarized. These results reveal a cortical physiological signature of the classically observed inverted-U relationship between task performance and arousal and that optimal detection exhibits enhanced sensory-evoked responses and reduced background synaptic activity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“By employing head-restrained preparations, a growing body of work has demonstrated that behavioral state fluctuations have a strong impact on neuronal gain and coding properties in primary sensory areas36,43,66–70 and modulate neuronal firing within brain-wide neuronal networks44,71–74.”
From Place and behavioral modulation of hippocampal neurons during immobility (2026) · cites this paper as [67]
In the citing paper
“These changes are sometimes coupled with overt movements such as whisking or locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“and can also be predicted with high accuracy by changes in pupil diameter or muscle tone while animals are sitting quietly”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“It has become increasingly clear that the waking state contains continuous transitions between numerous sub-states that are associated with significant changes in sensory-motor processing and behavioral performance”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“New animal studies reveal that rapid, ongoing, and behaviorally-relevant variations in waking state are not the exception, but rather the rule”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“These variations in state exert an effect on sensory evoked neural activity that can be as significant as variations in the sensory stimulus itself”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“During periods of quiet wakefulness (lacking overt movement), large low-frequency (< 10 Hz) fluctuations in the synaptic activity of somatosensory, visual, and auditory cortical neurons are frequently observed, and these oscillations are strongly suppressed by the initiation of whisking or locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“The transition from the quiet awake state to movement (locomotion and/or whisking) in head-fixed mice is always preceded by a strong suppression of cortical slow rhythms at the action potential, membrane potential, and field potential, levels”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“At these intermediate arousal levels, the average membrane potential of infragranular cortical neurons is at its most hyperpolarized, and least variable, level”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“At low levels of arousal, cortical neurons exhibit a high density of large, slow, rhythmic fluctuations in synaptic activity”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“For example, the presence of these different types of synaptic and membrane potential dynamics at low and high arousal states results in a U-shaped relationship between arousal and average cortical membrane potential in deep layer neurons of the auditory cortex”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Additionally, the relationship between cortical membrane potential and pupil diameter is influenced by the presence of slow oscillations at low arousal states and strong barrages of synaptic activity and/or tonic depolarization at high arousal states”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“The correlation between pupil diameter and the rate of sharp-wave ripples in the hippocampus (Figure 2D), further underscores the generality of this effect.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Increases in arousal from low to intermediate levels result in a suppression of these low (1–10 Hz) frequency activities, a decrease in membrane potential fluctuations, and thus a decrease in spontaneous activity”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Increases from intermediate to high arousal associated with brief (microdilations) or long-lasting increases in pupil diameter (often associated with locomotion), result in a depolarization of the membrane potential and increases in high-frequency membrane potential fluctuations from synaptic barrages”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“In addition to predicting changes in oscillatory network dynamics, changes in pupil diameter are reliably correlated with the average subthreshold membrane potential of at least some subtypes of neurons of the cortex, even in the absence of overt movement”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“During the period of movement (whisking or locomotion), neurons in somatosensory, visual, motor, and auditory cortex maintain an elevated firing rate, the degree of which varies across cell types.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“For example, a subset of layer 5 pyramidal neurons throughout the cortex is activated by movement”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“In contrast, in auditory cortex, the S/N peaks at intermediate arousal levels and may decrease strongly at high arousal levels and during locomotion”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“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 McGinley et al., 2015
In the citing paper
“Changes in arousal result in marked multiplicative gain modulation of auditory cortical responses, with the largest responses being evoked at intermediate levels of arousal”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Indeed, compensating for these rapid variations in state can significantly reduce variability in the amplitude and timing of both sensory evoked and behavioral responses (McGinley et al., 2015; Reimer et al., 2014).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“in L5 of auditory cortex, variability is increased during low and high arousal (either during quiet or active waking), but reduced at intermediate arousal (McGinley et al., 2015).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Monitoring of state through pupillometry reveals rapid (seconds) and continuous changes (Figure 2–4), indicating that a significant fraction of trial-to-trial variability in sensory evoked responses may be the result of variations in state.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“All three measures exhibited an inverted U relationship with arousal, with the largest, most reliable, and highest S/N ratio responses occurring at the same intermediate pupil diameters that revealed optimal performance on the auditory tone-in-complex sound detection task (Figure 7).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Importantly, the performance of mice on this auditory detection task was highly state dependent, with maximal hit rate, shortest lick latency, maximal discrimination, and least behavioral bias all occurring at intermediate pupil diameters (Figure 7C).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“At present, intracellular recordings from cortical neurons support both the sigmoidal and U model of arousal (e.g. Figure 1–3).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“In contrast to the sigmoidal model of activation, intracellular recordings in awake behaving rodents can reveal a pattern that is more consistent with a U-shaped dependence of average membrane potential and cortical activation on arousal (McGinley et al., 2015) (Figures 3C, 8C).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Interestingly, locomotion, as a sub-state of hyper-arousal, was associated with a strong decrease in behavioral performance, in comparison to intermediate arousal levels, which always occurred during stillness.”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Increases in arousal from low to intermediate, as indicated by increases in pupil diameter from small to medium, were associated with a large increase in task performance, while further increases in pupil diameter from intermediate to large were associated with a strong decrement in performance, whether or not the animal was walking at these large pupil diameters (Figure 7C).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
In the citing paper
“Interestingly, sound evoked responses in the auditory cortex also exhibited a strong relationship between arousal (pupil diameter) and the gain, amplitude, and reliability of these responses (Figure 7D).”
From Waking state: rapid variations modulate neural and behavioral responses (2015) · cites this paper as McGinley et al., 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The relation of strength of stimulus to rapidity of habit formation
Yerkes, R. M., & Dodson, J. D. (1908) · Journal of Comparative Neurology and Psychology · doi:10.1002/cne.920180503 · Cited by 6,911
Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472
The pupil as a measure of emotional arousal and autonomic activation
Bradley, M. M., Miccoli, L., Escrig, M. A., & Lang, P. J. (2008) · Psychophysiology · doi:10.1111/j.1469-8986.2008.00654.x · Cited by 1,919
Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity
van Vreeswijk, C., & Sompolinsky, H. (1996) · Science · doi:10.1126/science.274.5293.1724 · Cited by 1,831
Inverted-U–Shaped Dopamine Actions on Human Working Memory and Cognitive Control
Cools, R., & D'Esposito, M. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.03.028 · Cited by 1,673
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 · cited 6×
“Are the effects of arousal similar across cortical areas? In studies of the visual cortex, locomotion is associated with enhanced sensory-evoked visual responses (over non-locomotion) (Bennett et al., 2013; Niell and Stryker, 2010; Polack et al., 2013), while in auditory cortex locomotion results in significant suppre…”
BackgroundResultInfluentialBalanced inhibition underlies tuning and sharpens spike timing in auditory cortex
Wehr, M., & Zador, A. M. (2003) · Nature · doi:10.1038/nature02116 · Cited by 1,413
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
Cortical interneurons that specialize in disinhibitory control
Pi, H.-J., Hangya, B., Kvitsiani, D., Sanders, J. I., Huang, Z. J., & Kepecs, A. (2013) · Nature · doi:10.1038/nature12676 · Cited by 1,136
The Asynchronous State in Cortical Circuits
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010) · Science · doi:10.1126/science.1179850 · Cited by 1,106
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 729
Dasgupta, R., Seibt, F., & Beierlein, M. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0566-18.2018 · Cited by 47
Dissociation of task engagement and arousal effects in auditory cortex and midbrain
Saderi, D., Schwartz, Z. P., Heller, C. R., Pennington, J. R., & David, S. V. (2021) · eLife · doi:10.7554/elife.60153 · Cited by 49
Self-citationIntegration of somatosensory and motor-related information in the auditory system
Lohse, M., Zimmer-Harwood, P., Dahmen, J. C., & King, A. J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1010211 · Cited by 55
Relating Pupil Diameter and Blinking to Cortical Activity and Hemodynamics across Arousal States
Turner, K. L., Gheres, K. W., & Drew, P. J. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1244-22.2022 · Cited by 48
The Effect of Locomotion on Early Visual Contrast Processing in Humans
Benjamin, A. V., Wailes-Newson, K. H., Ma-Wyatt, A., Baker, D. H., & Wade, A. R. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1428-17.2017 · Cited by 47
Montijn, J. S., Goltstein, P. M., & Pennartz, C. M. (2015) · eLife · doi:10.7554/elife.10163 · Cited by 46
Sugden, A. U., Zaremba, J. D., Sugden, L. A., McGuire, K. L., Lutas, A., Ramesh, R. N., Alturkistani, O., Lensjø, K. K., Burgess, C. R., & Andermann, M. L. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0651-5 · Cited by 43
Focal Suppression of Distractor Sounds by Selective Attention in Auditory Cortex
Schwartz, Z. P., & David, S. V. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx288 · Cited by 45
Self-citationState-Dependent Subnetworks of Parvalbumin-Expressing Interneurons in Neocortex
García‐Junco‐Clemente, P., Tring, E., Ringach, D. L., & Trachtenberg, J. T. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.005 · Cited by 46
Meir, I., Katz, Y., & Lampl, I. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1159-18.2018 · Cited by 46
Neural correlates and determinants of approach–avoidance conflict in the prelimbic prefrontal cortex
Fernández-León, J. A., Engelke, D. S., Aquino-Miranda, G., Goodson, A., Rasheed, M. N., & Monte, F. H. D. (2021) · eLife · doi:10.7554/elife.74950 · Cited by 39
Pupil size as a window on neural substrates of cognitionPreprint
Joshi, S., & Gold, J. I. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/dvsme · Cited by 52
Cortical sensory processing across motivational states during goal-directed behavior
Matteucci, G., Guyoton, M., Mayrhofer, J. M., Auffret, M., Foustoukos, G., Petersen, C. C. H., & El‐Boustani, S. (2022) · Neuron · doi:10.1016/j.neuron.2022.09.032 · Cited by 43
Hersman, S., Allen, D. F., Hashimoto, M., Brito, S. I., & Anthony, T. E. (2020) · eLife · doi:10.7554/elife.53803 · Cited by 42
Disruption of cortical network activity by the general anaesthetic isoflurane
Hentschke, H., Raz, A., Krause, B. M., Murphy, C. A., & Banks, M. I. (2017) · British Journal of Anaesthesia · doi:10.1093/bja/aex199 · Cited by 43
Rasmussen, R. N., Jensen, M. H., & Heltberg, M. L. (2017) · Cell Systems · doi:10.1016/j.cels.2017.11.011 · Cited by 41
The Essential Complexity of Auditory Receptive Fields
Thorson, I. L., Liénard, J., & David, S. V. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004628 · Cited by 41
Self-citationTranslaminar Cortical Membrane Potential Synchrony in Behaving Mice
Zhao, W.-J., Kremkow, J., & Poulet, J. F. A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.05.026 · Cited by 42
Tartaglia, E. M., & Brunel, N. (2017) · Scientific Reports · doi:10.1038/s41598-017-12033-y · Cited by 42
Pupil-linked arousal with very light exercise: pattern of pupil dilation during graded exercise
Kuwamizu, R., Yamazaki, Y., Aoike, N., Ochi, G., Suwabe, K., & Soya, H. (2022) · The Journal of Physiological Sciences · doi:10.1186/s12576-022-00849-x · Cited by 49
Fazlali, Z., Ranjbar‐Slamloo, Y., Adibi, M., & Arabzadeh, E. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00014 · Cited by 39
McBride, E. G., Lee, S.-Y. J., & Callaway, E. M. (2019) · Current Biology · doi:10.1016/j.cub.2019.03.065 · Cited by 39
Performance in even a simple perceptual task depends on mouse secondary visual areas
Goldbach, H. C., Akitake, B., Leedy, C. E., & Histed, M. H. (2021) · eLife · doi:10.7554/elife.62156 · Cited by 45
Yang, H., Bari, B. A., Cohen, J. Y., & O’Connor, D. H. (2021) · eLife · doi:10.7554/elife.64327 · Cited by 46
Stability of ripple events during task engagement in human hippocampus
Chen, Y., Aponik-Gremillion, L., Bartoli, E., Yoshor, D., Sheth, S. A., & Foster, B. L. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109304 · Cited by 38
Brain State Dependence of Hippocampal Subthreshold Activity in Awake Mice
Hulse, B. K., Lubenov, E. V., & Siapas, A. G. (2017) · Cell Reports · doi:10.1016/j.celrep.2016.11.084 · Cited by 40
Hearing in Complex Environments: Auditory Gain Control, Attention, and Hearing Loss
Auerbach, B. D., & Gritton, H. J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.799787 · Cited by 42
Triple dissociation of visual, auditory and motor processing in mouse primary visual cortex
Lohuis, M. N. O., Marchesi, P., Olcese, U., & Pennartz, C. M. A. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01564-5 · Cited by 49
Fernandez, L. M. J., Comte, J., Merre, P. L., Lin, J., Salin, P. A., & Crochet, S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw311 · Cited by 44
Winn, M. B., & Teece, K. H. (2022) · Journal of Speech Language and Hearing Research · doi:10.1044/2022_jslhr-21-00631 · Cited by 40
Task-related EEG and HRV entropy factors under different real-world fatigue scenarios
Lin, C., Nascimben, M., King, J., & Wang, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.043 · Cited by 39
Pennartz, C. M. A., Oude Lohuis, M. N., & Olcese, U. (2023) · Philosophical Transactions of the Royal Society B · doi:10.1098/rstb.2022.0336 · Cited by 38
Adaptive control of synaptic plasticity integrates micro- and macroscopic network function
Scott, D. N., & Frank, M. J. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01374-6 · Cited by 40
Olcese, U., Bos, J. J., Vinck, M. A., Lankelma, J. V., Mourik-Donga, L. A. V., Schlumm, F., & Pennartz, C. M. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.4201-15.2016 · Cited by 40
Schwartz, Z. P., Buran, B. N., & David, S. V. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00595.2019 · Cited by 39
Self-citationFink, L., Hurley, B. K., Geng, J. J., & Janata, P. (2018) · Journal of Eye Movement Research · doi:10.16910/jemr.11.2.12 · Cited by 41
Fingelkurts, A. A., & Fingelkurts, A. A. (2022) · Applied Sciences · doi:10.3390/app12199560 · Cited by 43
Pupil dynamics during very light exercise predict benefits to prefrontal cognition
Kuwamizu, R., Yamazaki, Y., Aoike, N., Hiraga, T., Hata, T., Yassa, M. A., & Soya, H. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120244 · Cited by 41
Correlation between Pupil Size and Subjective Passage of Time in Non-Human Primates
Suzuki, T. W., Kunimatsu, J., & Tanaka, M. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.2533-16.2016 · Cited by 35
Voss, L. J., García, P. S., Hentschke, H., & Banks, M. I. (2019) · Anesthesiology · doi:10.1097/aln.0000000000002554 · Cited by 38
Functional flexibility in cortical circuits
Cardin, J. A. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.008 · Cited by 37
Selective Suppression of Local Circuits during Movement Preparation in the Mouse Motor Cortex
Hasegawa, M., Majima, K., Itokazu, T., Maki, T., Albrecht, U.-R., Castner, N., Izumo, M., Sohya, K., Sato, T. K., Kamitani, Y., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.02.043 · Cited by 36
Chandelier cell anatomy and function reveal a variably distributed but common signalPreprint
Schneider-Mizell, C. M., Bodor, Á. L., Collman, F., Brittain, D., Bleckert, A., Dorkenwald, S., Turner, N. L., Macrina, T., Lee, K., Lu, R., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.31.018952 · Cited by 37
Cortico-thalamo-cortical interactions modulate electrically evoked EEG responses in mice
Claar, L. D., Rembado, I., Kuyat, J. R., Russo, S., Marks, L. C., Olsen, S. R., & Koch, C. (2023) · eLife · doi:10.7554/elife.84630.3 · Cited by 36
Sleep fMRI with simultaneous electrophysiology at 9.4 T in male mice
Yu, Y., Qiu, Y., Li, G., Zhang, K., Bo, B., Pei, M., Ye, J., Thompson, G. J., Cang, J., Fang, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37352-9 · Cited by 35
Claustrum neurons projecting to the anterior cingulate restrict engagement during sleep and behavior
Atlan, G., Matosevich, N., Peretz‐Rivlin, N., Marsh‐Yvgi, I., Zelinger, N., Chen, E., Kleinman, T., Bleistein, N., Sheinbach, E., Groysman, M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48829-6 · Cited by 77
Dynamic states of population activity in prefrontal cortical networks of freely-moving macaque
Milton, R., Shahidi, N., & Dragoi, V. (2020) · Nature Communications · doi:10.1038/s41467-020-15803-x · Cited by 38
Augustinaite, S., & Kühn, B. (2020) · Current Biology · doi:10.1016/j.cub.2020.07.069 · Cited by 35
Task-induced modulations of neuronal activity along the auditory pathway
Franceschi, G. D., & Barkat, T. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110115 · Cited by 37
Neural Variability Limits Adolescent Skill Learning
Caras, M. L., & Sanes, D. H. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2878-18.2019 · Cited by 36
Response nonlinearities in networks of spiking neurons
Sanzeni, A., Histed, M. H., & Brunel, N. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008165 · Cited by 31
Multisensory task demands temporally extend the causal requirement for visual cortex in perception
Lohuis, M. N. O., Pie, J. L., Marchesi, P., Montijn, J. S., de Kock, C. P. J., Pennartz, C. M. A., & Olcese, U. (2022) · Nature Communications · doi:10.1038/s41467-022-30600-4 · Cited by 32
A Dynamic Link between Respiration and Arousal
Kluger, D. S., Groß, J., & Keitel, C. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1173-24.2024 · Cited by 38
Hadzipasic, M., Ni, W., Nagy, M., Steenrod, N., McGinley, M. J., Kaushal, A., Thomas, E. V., McCormick, D. A., & Horwich, A. L. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1616832113 · Cited by 36
Self-citationCorrelation of Synaptic Inputs in the Visual Cortex of Awake, Behaving Mice
Arroyo, S., Bennett, C., & Hestrin, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.008 · Cited by 33
Kumar, M., Xiong, S., Tzounopoulos, T., & Anderson, C. T. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1339-18.2018 · Cited by 33
McHaney, J. R., Tessmer, R., Roark, C. L., & Chandrasekaran, B. (2021) · Brain and Language · doi:10.1016/j.bandl.2021.105010 · Cited by 31
Alpha EEG Activity and Pupil Diameter Coupling during Inactive Wakefulness in Humans
Montefusco‐Siegmund, R., Schwalm, M., Jubal, E. R., Devia, C., Egaña, J. I., & Maldonado, P. (2022) · eNeuro · doi:10.1523/eneuro.0060-21.2022 · Cited by 33
Rowland, J. M., van der Plas, T. L., Loidolt, M., Lees, R. M., Keeling, J., Dehning, J., Akam, T., Priesemann, V., & Packer, A. M. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01413-5 · Cited by 36
Kjærby, C., Rasmussen, R. N., Andersen, M., & Nedergaard, M. (2017) · Neurochemical Research · doi:10.1007/s11064-017-2195-y · Cited by 34
Incorporating behavioral and sensory context into spectro-temporal models of auditory encoding
David, S. V. (2017) · Hearing Research · doi:10.1016/j.heares.2017.12.021 · Cited by 33
Self-citationSkaansar, J. F., Laeng, B., & Danielsen, A. (2019) · Music Perception An Interdisciplinary Journal · doi:10.1525/mp.2019.37.2.111 · Cited by 31
Sörensen, L. K. A., Bohté, S. M., Slagter, H. A., & Scholte, H. S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009976 · Cited by 35
Colbrook, M. J., Li, Q., Raut, R. V., & Townsend, A. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-023-09135-w · Cited by 38
Network mechanisms of ongoing brain activity’s influence on conscious visual perception
Wu, Y., Podvalny, E., Levinson, M., & He, B. J. (2024) · Nature Communications · doi:10.1038/s41467-024-50102-9 · Cited by 37
Decoding cortical brain states from widefield calcium imaging data using visibility graph
Zhu, L., Lee, C. R., Margolis, D. J., & Najafizadeh, L. (2018) · Biomedical Optics Express · doi:10.1364/boe.9.003017 · Cited by 32
Acetylcholine acts on songbird premotor circuitry to invigorate vocal output
Jaffe, P. I., & Brainard, M. S. (2020) · eLife · doi:10.7554/elife.53288 · Cited by 31
Kouvaros, S., Kumar, M., & Tzounopoulos, T. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa005 · Cited by 28
Rapid Cortical Adaptation and the Role of Thalamic Synchrony during Wakefulness
Wright, N. C., Borden, P. Y., Liew, Y. J., Bolus, M. F., Stoy, W. M., Forest, C. R., & Stanley, G. B. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.3018-20.2021 · Cited by 27
Independent response modulation of visual cortical neurons by attentional and behavioral states
Kanamori, T., & Mrsic‐Flogel, T. D. (2022) · Neuron · doi:10.1016/j.neuron.2022.08.028 · Cited by 33
Ongoing movement controls sensory integration in the dorsolateral striatum
de la Torre‐Martínez, R., Ketzef, M., & Silberberg, G. (2023) · Nature Communications · doi:10.1038/s41467-023-36648-0 · Cited by 32
Shepard, K. N., Chong, K. K., & Liu, R. C. (2016) · eNeuro · doi:10.1523/eneuro.0318-16.2016 · Cited by 31
Choice History Biases Subsequent Evidence AccumulationPreprint
Urai, A. E., de Gee, J. W., Tsetsos, K., & Donner, T. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/251595 · Cited by 32
Short-Term Monocular Deprivation Enhances Physiological Pupillary Oscillations
Binda, P., & Lunghi, C. (2017) · Neural Plasticity · doi:10.1155/2017/6724631 · Cited by 32
Elucidating Neuronal Mechanisms Using Intracellular Recordings during Behavior
Lee, A. K., & Brecht, M. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.03.014 · Cited by 34
Chen, C., & Song, S. (2019) · Communications Biology · doi:10.1038/s42003-019-0602-4 · Cited by 32
Schriver, B. J., Perkins, S. M., Sajda, P., & Wang, Q. (2020) · Psychophysiology · doi:10.1111/psyp.13565 · Cited by 29
Afrashteh, N., Inayat, S., Bermudez-Contreras, E. J., Luczak, A., McNaughton, B. L., & Mohajerani, M. H. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110081 · Cited by 29
He, H., Hong, L., & Sajda, P. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011081 · Cited by 30
Air-Track: a real-world floating environment for active sensing in head-fixed mice
Nashaat, M. A., Oraby, H., Sachdev, R. N. S., Winter, Y., & Larkum, M. E. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00088.2016 · Cited by 30
Buckley, C. L., & Toyoizumi, T. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005926 · Cited by 28
The neural hierarchy of consciousness: A theoretical model and review on neurophysiology and NCCs
Almeida, V. N. (2022) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2022.108202 · Cited by 29
Yang, Z., Williams, S. D., Beldzik, E., Anakwe, S. U., Schimmelpfennig, E., & Lewis, L. D. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02098-8 · Cited by 32
Local and global contributions to hemodynamic activity in mouse cortex
Pisauro, M. A., Benucci, A., & Carandini, M. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00125.2016 · Cited by 28
Translaminar recurrence from layer 5 suppresses superficial cortical layers
Onodera, K., & Kato, H. (2022) · Nature Communications · doi:10.1038/s41467-022-30349-w · Cited by 26
Diverse effects of stimulus history in waking mouse auditory cortex
Phillips, E. A. K., Schreiner, C. E., & Hasenstaub, A. R. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00094.2017 · Cited by 29
Schwalm, M., & Jubal, E. R. (2017) · eNeuro · doi:10.1523/eneuro.0293-16.2017 · Cited by 29
Larsen, R. S., Turschak, E., Daigle, T. L., Zeng, H., Zhuang, J., & Waters, J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/502013 · Cited by 32
Andrews, J. P., Yue, Z., Ryu, J. H., Neske, G. T., McCormick, D. A., & Blumenfeld, H. (2018) · Experimental Neurology · doi:10.1016/j.expneurol.2018.11.008 · Cited by 27
Self-citationNeuronal Avalanches in Input and Associative Layers of Auditory Cortex
Bowen, Z., Winkowski, D. E., Seshadri, S., Plenz, D., & Kanold, P. O. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00045 · Cited by 27
Movement-Related Modulation in Mouse Auditory Cortex Is Widespread Yet Locally Diverse
Morandell, K., Yin, A., Rio, R. T. D., & Schneider, D. M. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1227-23.2024 · Cited by 28
State-dependent geometry of population activity in rat auditory cortex
Kobak, D., Pardo‐Vázquez, J. L., Valente, M., Machens, C. K., & Renart, A. (2019) · eLife · doi:10.7554/elife.44526 · Cited by 25
Pupillometric investigation into the speed‐accuracy trade‐off in a visuo‐motor aiming task
Naber, M., & Murphy, P. (2019) · Psychophysiology · doi:10.1111/psyp.13499 · Cited by 28
Spontaneous Fluctuations in Visual Cortical Responses Influence Population Coding Accuracy
Gutnisky, D. A., Beaman, C. B., Lew, S. E., & Dragoi, V. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv312 · Cited by 29
Sabri, E., & Arabzadeh, E. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.09.016 · Cited by 24
Li, Y., Ward, M., Richardson, R. M., G’Sell, M., & Ghuman, A. S. (2020) · Nature Communications · doi:10.1038/s41467-020-17729-w · Cited by 24
Totah, N. K. B., Logothetis, N. K., & Eschenko, O. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00677.2020 · Cited by 32
Vivaldo, C. A., Lee, J., Shorkey, M., Keerthy, A., & Rothschild, G. (2023) · PLoS Biology · doi:10.1371/journal.pbio.3002277 · Cited by 37
Pupil-based States of Brain Integration across Cognitive States
Mäki-Marttunen, V. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.07.016 · Cited by 26
Paraskevoudi, N., & SanMiguel, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-96346-z · Cited by 26
Metadata
- Authors
- McGinley, M. J., David, S. V., & McCormick, D. A.
- Affiliations
- Yale University (US)
- Oregon Health & Science University (US)
- Corresponding author
- McGinley, Matthew J.
- McCormick, David A.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 87
- Issue
- 1
- Pages
- 179-192
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC4631312
- OpenAlex
- W1519578210
- Cited by
- 728
- References
- 57
- Citations by year
- 2026: 58 · 2025: 73 · 2024: 80 · 2023: 89 · 2022: 67 · 2021: 73 · 2020: 61 · 2019: 53 · 2018: 64 · 2017: 52 · 2016: 48 · 2015: 10
- FWCI
- 34.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Public Health Service (5R01N2026143)
- National Institute on Deafness and Other Communication Disorders (R01DC015803, 1F132DC012449, F32DC012449, R00DC010439)
- National Institute of Neurological Disorders and Stroke (R01NS026143)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- signal detection, arousal, inverted U-shaped relationship, auditory cortex, tone-in-noise detection, pupillometry, slow oscillations, hyperpolarization, depolarization, synaptic activity
- MeSH terms
- Animals, Arousal, Auditory Cortex, Auditory Perception, Membrane Potentials, Neurons, 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 ›
Are you an author, or do you see a problem with this page? Request a review