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Cited by 327Neural dynamics and brain functionNeuroscience and Neuropharmacology ResearchVisual perception and processing mechanisms

Mechanisms underlying gain modulation in the cortex

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Ferguson, Katie A., Cardin, Jessica A.·2020·Nature Reviews Neuroscience
DOI 10.1038/s41583-019-0253-yPMC PMC7408409
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Abstract

Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition. Changes in cortical gain enable neurons to respond adaptively to changing inputs. In this Review, Ferguson and Cardin describe the mechanisms that modulate cortical gain, and its effects on and relevance for cognition and behaviour.

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TL;DR

This review synthesizes how cortical neural gain is modulated by attention, arousal, locomotion, learning, and neuromodulatory input, converging on common cellular mechanisms including GABAergic inhibition, synaptic membrane-potential fluctuations, and changes in conductance. It highlights GABAergic interneurons as key targets of neuromodulation and mediators of state-dependent gain modulation, while noting unresolved links to perception, population coding, and network-level effects.

Key findings

  • Gain modulation is a canonical cortical computation that regulates neuronal sensitivity while preserving selectivity for input features; it is engaged by external sensory context, such as contrast-invariant orientation tuning, and by internal states including attention, arousal, locomotion, and learning.
  • Multiple influences converge on shared cellular mechanisms: synaptically driven fluctuations in membrane potential, changes in membrane conductance and depolarization, and GABAergic inhibition; GABAergic inhibition can adjust response gain without altering local selectivity or input gain.
  • GABAergic interneuron subtypes have cell type-specific and state-dependent roles: VIP+ interneurons are activated by arousal and locomotion and suppress SST+ interneurons, SST+ interneurons can enhance gain modulation during adaptation, and effects vary by cortical layer and area—for example, locomotion increases visual response gain in mouse V1 but reduces auditory response gain in A1.
  • Neuromodulators regulate gain, often via inhibitory interneurons: acetylcholine and noradrenaline are largely gain-enhancing, serotonin predominantly reduces gain, and dopamine can modulate response amplitude and selectivity, but effects are heterogeneous and many cellular and network mechanisms remain unclear.

Methods

  • Design: narrative review of cortical gain modulation mechanisms, integrating findings from electrophysiology, imaging, optogenetics, computational modeling, and behavioral studies.
  • Data or sample: no primary dataset; synthesizes published literature across rodent, cat, non-human primate, and human cortex.
  • Main analysis: conceptual synthesis of cellular, circuit, and behavioral evidence; no systematic review or meta-analytic method is described.

Limitations

  • The paper states that a detailed mechanistic understanding of behavioural- and arousal-state interactions remains incomplete; the causal relationship between gain modulation and psychophysical performance remains to be fully established; network-level impacts, single-neuron-to-population coding relationships, reliability and repeatability of gain modulation, and contributions to perceptual and cognitive performance are not well understood.
  • Evident from the text: as a narrative review, it does not provide a systematic search strategy or quantitative synthesis, and it draws on heterogeneous species, preparations, and manipulation methods—including conflicting optogenetic results and sensitivity to manipulation parameters—which complicates direct comparison across findings.

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Crossref · 10.1038/s41583-019-0253-y

Cites

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Claims the paper makes

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  • Premisecitation

    Contrast-invariant orientation tuning of cortical neurons for visual stimuli is a well-characterized example of stable encoding of orientation regardless of changes in contrast.

    “Contrast-invariant orientation tuning of cortical neurons for visual stimuli is a well-characterized example of stable encoding of one feature (in this case, orientation), regardless of changes in stimulus context (in this case, contrast)10–12.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [10] Finn, I. M., Priebe, N. J. & Ferster, D. The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex. Neuron 54, 137–152 (2007). source

      Not checked in the free run.

    • cites [11] Sclar, G. & Freeman, R. D. Orientation selectivity in the cat’s striate cortex is invariant with stimulus contrast. Exp. Brain Res. 46, 457–461 (1982). source

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    • cites [12] Skottun, B. C., Bradley, A., Sclar, G., Ohzawa, I. & Freeman, R. D. The effects of contrast on visual orientation and spatial frequency discrimination: a comparison of single cells and behavior. J. Neurophysiol. 57, 773–786 (1987). source

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  • Interpretationreasoning

    Regulation of neural gain provides an integration mechanism whereby information from multiple sources can be non-linearly combined via multiplicative modulation of the cell's response to inputs.

    “Regulation of neural gain thus provides an integration mechanism whereby information from multiple sources can be non-linearly combined via multiplicative modulation of the cell’s response to inputs (BOX 1).”
    Quote in the paperSubstantial claim
  • Premisecitation

    Several studies have found good agreement between population-level responses of gain-modulated neurons and psychophysical performance in visual contrast discrimination in humans and non-human primates.

    “Indeed, several studies have found good agreement between the population-level responses of gain-modulated neurons (such as the population-average contrast response function) and psychophysical performance (for example, in visual contrast discrimination) in humans and non-human primates12,20–24.”
    Quote in the paperSubstantial claim0 of 6 sources checked
    • cites [12] Skottun, B. C., Bradley, A., Sclar, G., Ohzawa, I. & Freeman, R. D. The effects of contrast on visual orientation and spatial frequency discrimination: a comparison of single cells and behavior. J. Neurophysiol. 57, 773–786 (1987). source

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  • Interpretationcitation

    Correlations between the gain of neural responses and psychophysical performance support the hypothesis that gain modulation may mediate the trade-off between sensitivity to all salient signals and selectivity for specific signals.

    “Correlations between the gain of neural responses and psychophysical performance support the hypothesis that gain modulation may mediate the trade-off between sensitivity to all salient signals and selectivity for specific signals25.”
    Quote in the paperSubstantial claim0 of 1 sources checked
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  • Premisecitation

    Gain modulation mechanisms are robustly engaged by changes in internal state.

    “Gain modulation mechanisms are also robustly engaged by changes in internal state32–35.”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [32] Niell, C. M. & Stryker, M. P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479 (2010). source

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    Premisecitation

    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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [33] Vinck, M., Batista-Brito, R., Knoblich, U. & Cardin, J. A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron 86, 740–754 (2015). This paper shows that arousal and locomotion differentially regulate neural activity and sensory response gain in mouse V1. source

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    Premisecitation

    Gain-modulated cells are suited to perform multimodal computations, such as conversions from sensory-centred into motor-centred reference frames, and for the generation of invariant responses to input features despite contextual variability.

    “Gain-modulated cells are ideally suited to perform multimodal computations, such as conversions from sensory-centred into motor-centred reference frames, and for the generation of invariant responses to input features despite contextual variability6–9.”
    Quote in the paperSubstantial claim0 of 4 sources checked
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    Premisecitation

    In non-human primate visual system, visual spatial attention within the receptive field of a recorded neuron enhances both contrast gain and response gain.

    “Attentional modulation of neural gain has been particularly well studied in the non-human primate visual system1,42,43, in which visual spatial attention within the receptive field of a recorded neuron enhances both contrast gain (that is, scaling of that neuron’s stimulus-response relationship along the contrast axis)43 and response gain (that is, multiplicative transformation of responses to all”
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    Premisecitation

    In high-order visual cortex of non-human primates, properties that must be decoded separately are combined multiplicatively, whereas those that must be integrated are combined additively.

    “In the high-order visual cortex of non-human primates, properties that must be decoded separately (for example, object identity and image attributes) are combined multiplicatively, whereas those that must be integrated (for example, parts of an object) are combined additively31.”
    Quote in the paperSubstantial claim0 of 1 sources checked
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    Premisecitation

    Locomotion is correlated with reductions in response gain in primary auditory cortex (A1), suggesting state-dependent gain modulation may vary between different brain areas.

    “By contrast, locomotion is correlated with reductions in response gain in the primary auditory cortex (A1), suggesting that state-dependent gain modulation may vary between different brain areas38,39.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [38] Schneider, D. M., Nelson, A. & Mooney, R. A synaptic and circuit basis for corollary discharge in the auditory cortex. Nature 513, 189–194 (2014). This article shows that locomotion decreases the response gain in the mouse primary auditory cortex via inhibitory interneuron actions in the local cortical circuit. source

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    Premisecitation

    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.”
    Quote in the paperNot a substantial claim0 of 4 sources checked
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      Not checked in the free run.

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    Premisecitation

    Vm fluctuations are regulated by environmental factors and stimulus conditions, as well as by several neuromodulatory influences.

    “Importantly, Vm fluctuations are regulated by environmental factors and stimulus conditions, as well as by several neuromodulatory influences61.”
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    Premisecitation

    Locomotion paired with visual stimuli dramatically enhances recovery of visual responses in mice with monocular deprivation in an NMDA receptor-dependent manner, whereas locomotion or visual stimuli alone are insufficient to rescue such responses.

    “Locomotion paired with visual stimuli dramatically enhances the recovery of visual responses in mice with monocular deprivation in an NMDA receptor-dependent manner, whereas locomotion or visual stimuli alone are insufficient to rescue such responses56.”
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    Premisecitation

    Experiments and computational models demonstrate that attention amplifies and stabilizes target cell responses, reducing their variance across trials.

    “Experiments and computational models further demonstrate that attention amplifies and stabilizes target cell responses, reducing their variance across trials50,51.”
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    Premisecitation

    The degree to which the synaptic input underlying Vm fluctuations is temporally correlated can vary with overall drive to a network or with firing rates and is regulated by behavioural state transitions during wakefulness.

    “The degree to which the synaptic input underlying Vm fluctuations is temporally correlated can vary with overall drive to a network or with firing rates70 and is regulated by behavioural state transitions during wakefulness29,71–74.”
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    Premisecitation

    Correlations between pairs of excitatory inputs or pairs of inhibitory inputs increase fluctuations in synaptic drive, whereas excitatory-inhibitory correlations decrease fluctuations.

    “Correlations between pairs of excitatory inputs or pairs of inhibitory inputs increase fluctuations in synaptic drive, whereas excitatory–inhibitory correlations decrease fluctuations9.”
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    The sensitivity of individual neurons to synaptic fluctuations can vary with neural subtype or cortical area and with biophysical cellular properties, such as membrane capacitance and conductance.

    “Furthermore, the sensitivity of individual neurons to synaptic fluctuations can vary with neural subtype or cortical area and with biophysical cellular properties, such as membrane capacitance and conductance89–93.”
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    “Cholinergic receptors are expressed by inhibitory and excitatory cortical neurons119,124,158–164, as well as on the terminals of thalamocortical neurons165–169.”
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    Premisecitation

    Optogenetic stimulation of endogenous ACh release in mouse V1 desynchronizes spiking and enhances visual perceptual performance without altering overall firing rates.

    “In contrast to the effects of pharmacological agents, optogenetic stimulation of endogenous ACh release in mouse V1 desynchronizes spiking and enhances visual perceptual performance, without altering overall firing rates175.”
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    Optical stimulation of endogenous ACh release in S1 leads to activation of mAChRs on excitatory and inhibitory neurons in L4 and activation of nAChRs, presumably on inhibitory interneurons, in superficial layers, causing overall suppression of cortical activity.

    “Optical stimulation of endogenous ACh release in S1 leads to activation of mAChRs on excitatory and inhibitory neurons in L4 and activation of nAChRs, presumably on inhibitory interneurons, in the superficial layers, causing overall suppression of cortical activity184.”
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    Cortical pyramidal neurons show heterogeneous expression of serotonin receptors and therefore may exhibit varied responses to activation of serotonergic afferents.

    “However, cortical pyramidal neurons show heterogeneous expression of serotonin receptors and therefore may exhibit varied responses to activation of serotonergic afferents201.”
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    Blocking noradrenergic receptors results in hyperpolarization of pyramidal neurons, decreases their firing and prevents locomotion-induced increases in visual response gain.

    “blocking noradrenergic receptors results in hyperpolarization of pyramidal neurons, decreases their firing and prevents locomotion-induced increases in visual response gain73.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [73] Polack, P. O., Friedman, J. & Golshani, P. Cellular mechanisms of brain state-dependent gain modulation in visual cortex. Nat. Neurosci. 16, 1331–1339 (2013). source

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    Premisecitation

    Cholinergic input to cortex increases SST+ interneuron activity, thus increasing inhibition of PV+ cells and pyramidal neurons and desynchronizing cortical networks and synaptic inputs to individual neurons.

    “Cholinergic input to the cortex increases SST+ interneuron activity, thus increasing inhibition of PV+ cells and pyramidal neurons and desynchronizing cortical networks75 and synaptic inputs to individual neurons.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [75] Chen, N., Sugihara, H. & Sur, M. An acetylcholine-activated microcircuit drives temporal dynamics of cortical activity. Nat. Neurosci. 18, 892–902 (2015). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    Gain modulation serves both adaptation to varying input ranges and enhancing salience of relevant information.

    “Gain modulation serves both of these functions.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Activity of interneurons in L1 is enhanced by nAChR activation, potentially leading to reductions in activity of postsynaptic PV+ interneurons in L2/3.

    “The activity of interneurons in L1 is also enhanced by nAChR activation, potentially leading to reductions in the activity of postsynaptic PV+ interneurons in L2/3162.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [162] Letzkus, J. J. et al. A disinhibitory microcircuit for associative fear learning in the auditory cortex. Nature 480, 331–335 (2011). source

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    Premisecitation

    In response to repeated auditory tones, excitatory neuron activity in mouse A1 adapts in a frequency-dependent manner, whereas responses of PV+ interneurons are stable and those of SST+ interneurons increase.

    “For example, in response to repeated auditory tones, excitatory neuron activity in mouse A1 adapts in a frequency-dependent manner, whereas the responses of PV+ interneurons are stable and those of SST+ interneurons increase.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [105] Natan, R. G., Rao, W. & Geffen, M. N. Cortical interneurons differentially shape frequency tuning following adaptation. Cell Rep. 21, 878–890 (2017). This paper shows that distinct cortical interneuron populations differently modulate the gain of frequency-tuned excitatory responses during adaptation. source

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    Premisecitation

    Adaptation enables high sensitivity to small changes in stimulus features over a large range of intensities and is found across sensory systems.

    “Adaptation enables high sensitivity to small changes in stimulus features over a large range of intensities, and is found across sensory systems5,17,26,105,210–216.”
    Quote in the paperNot a substantial claim0 of 10 sources checked
    • cites [5] Carandini, M. & Heeger, D. J. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2011). source

      Not checked in the free run.

    • cites [17] Baccus, S. A. & Meister, M. Fast and slow contrast adaptation in retinal circuitry. Neuron 36, 909–919 (2002). source

      Not checked in the free run.

    • cites [26] Natan, R. G., Carruthers, I. M., Mwilambwe-Tshilobo, L. & Geffen, M. N. Gain control in the auditory cortex evoked by changing temporal correlation of sounds. Cereb. Cortex 27, 2385–2402 (2017).

      Not checked in the free run.

    • cites [105] Natan, R. G., Rao, W. & Geffen, M. N. Cortical interneurons differentially shape frequency tuning following adaptation. Cell Rep. 21, 878–890 (2017). This paper shows that distinct cortical interneuron populations differently modulate the gain of frequency-tuned excitatory responses during adaptation. source

      Not checked in the free run.

    • cites [210] Demb, J. B. Multiple mechanisms for contrast adaptation in the retina. Neuron 36, 781–783 (2002). source

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    • cites [211] Farley, B. J., Quirk, M. C., Doherty, J. J. & Christian, E. P. Stimulus-specific adaptation in auditory cortex is an NMDA-independent process distinct from the sensory novelty encoded by the mismatch negativity. J. Neurosci. 30, 16475–16484 (2010). source

      Not checked in the free run.

    • cites [212] Fishman, Y. I. & Steinschneider, M. Searching for the mismatch negativity in primary auditory cortex of the awake monkey: deviance detection or stimulus specific adaptation? J. Neurosci. 32, 15747–15758 (2012). source

      Not checked in the free run.

    • cites [213] Kohn, A. & Movshon, J. A. Neuronal adaptation to visual motion in area MT of the macaque. Neuron 39, 681–691 (2003). source

      Not checked in the free run.

    • cites [214] Szymanski, F. D., Garcia-Lazaro, J. A. & Schnupp, J. W. Current source density profiles of stimulus-specific adaptation in rat auditory cortex. J. Neurophysiol. 102, 1483–1490 (2009). source

      Not checked in the free run.

    • cites [215] Ulanovsky, N., Las, L. & Nelken, I. Processing of low-probability sounds by cortical neurons. Nat. Neurosci. 6, 391–398 (2003). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Increase in SST+ interneuron activity following adaptation results in enhanced gain modulation of excitatory neuron auditory responses, whereas PV+ interneurons do not affect adaptation-induced response gain.

    “The increase in SST+ interneuron activity following adaptation results in enhanced gain modulation of excitatory neuron auditory responses, whereas PV+ interneurons do not affect the adaptation-induced response gain105.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [105] Natan, R. G., Rao, W. & Geffen, M. N. Cortical interneurons differentially shape frequency tuning following adaptation. Cell Rep. 21, 878–890 (2017). This paper shows that distinct cortical interneuron populations differently modulate the gain of frequency-tuned excitatory responses during adaptation. source

      Not checked in the free run.

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    Premisecitation

    Increased inhibitory input reduces sensitivity of excitatory neurons to changes in auditory input and expands dynamic range of neural responses.

    “Increased inhibitory input reduces the sensitivity of excitatory neurons to changes in auditory input and expands the dynamic range of neural responses (BOX 1), and also potentially increases the efficiency of encoding217.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [217] Barlow, H. in Sensory Communication (MIT Press, 1961). source

      Not checked in the free run.

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    Interpretationcitation

    Gain increases may optimize signal discrimination in presence of external noise by facilitating attractor dynamics and promoting a winner-take-all mechanism.

    “Gain increases may optimize signal discrimination in the presence of external noise by facilitating attractor dynamics218 and promoting a winner-take-all mechanism219,220.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [218] Niyogi, R. K. & Wong-Lin, K. Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making. PLOS Comput. Biol. 9, e1003099 (2013). source

      Not checked in the free run.

    • cites [219] Busse, L., Wade, A. R. & Carandini, M. Representation of concurrent stimuli by population activity in visual cortex. Neuron 64, 931–942 (2009). source

      Not checked in the free run.

    • cites [220] Hahnloser, R. H., Douglas, R. J. & Hepp, K. Attentional recruitment of inter-areal recurrent networks for selective gain control. Neural Comput. 14, 1669–1689 (2002). source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Using two separate decoders during still and active periods does not improve stimulus prediction, indicating that a model with a single mode of gain modulation best reflects improvements in encoding between the two states.

    “Using two separate decoders during still and active periods does not improve stimulus prediction, indicating that a model with a single mode of gain modulation best reflects the improvements in encoding between the two states27,37.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [27] Mineault, P. J., Tring, E., Trachtenberg, J. T. & Ringach, D. L. Enhanced spatial resolution during locomotion and heightened attention in mouse primary visual cortex. J. Neurosci. 36, 6382–6392 (2016). This paper demonstrates that the relative gain of visual responses between quiescence and locomotion is heterogeneous across cells and depends on the (spatial-frequency) tuning properties of the cell. source

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    • cites [37] Dadarlat, M. C. & Stryker, M. P. Locomotion enhances neural encoding of visual stimuli in mouse V1 J. Neurosci. 37, 3764–3775 (2017). This paper demonstrates that locomotion enhances neural encoding of visual stimuli through increased firing rates and decreased noise correlations across the population. source

      Not checked in the free run.

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    Interpretationcitation

    A key role of gain modulation may be to increase information transmission and provide computational efficiency within a neural network under limited resources.

    “Finally, a key role of gain modulation may be to increase information transmission and provide computational efficiency within a neural network222 under the constraint of limited resources.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [222] Schwartz, O. & Simoncelli, E. P. Natural signal statistics and sensory gain control. Nat. Neurosci. 4, 819–825 (2001). source

      Not checked in the free run.

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    Premisecitation

    A cortical circuit model of decision-making indicates dynamic co-modulation of both excitatory and inhibitory gain produces a more stable and robust network, allowing more flexible and cognitively demanding decision-making, and gain modulation can compensate for weaker recurrent excitation.

    “A cortical circuit model of decision-making indicates that dynamic co-modulation of both excitatory and inhibitory gain produces a more stable and robust network, allowing for more flexible and cognitively demanding decision-making, and that gain modulation can compensate for weaker recurrent excitation218.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [218] Niyogi, R. K. & Wong-Lin, K. Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making. PLOS Comput. Biol. 9, e1003099 (2013). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Gain regulation has been suggested to enable networks of neurons to produce distributed representations of stimulus features.

    “Gain regulation has been suggested to enable networks of neurons to produce distributed representations of stimulus features223, and to allow downstream decoders to be optimized for diverse stimuli by separating those features224.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [223] Willmore, B. D., Bulstrode, H. & Tolhurst, D. J. Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1). Vis. Res. 54, 49–60 (2012). source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Gain regulation has been suggested to allow downstream decoders to be optimized for diverse stimuli by separating those features.

    “Gain regulation has been suggested to enable networks of neurons to produce distributed representations of stimulus features223, and to allow downstream decoders to be optimized for diverse stimuli by separating those features224.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [224] Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J. & Cohen, M. R. Learning and attention reveal a general relationship between population activity and behavior. Science 359, 463–465 (2018). source

      Not checked in the free run.

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    Premisecitation

    Spontaneous fluctuations in network activity modulate gain of neuronal responses homogeneously across excitatory cells, whereas visual stimulus contrast modulates gain of individual neurons independently.

    “Indeed, recent work suggests that spontaneous fluctuations in network activity modulate the gain of neuronal responses homogeneously across excitatory cells, whereas visual stimulus contrast modulates the gain of individual neurons independently225.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [225] Lee, S., Park, J. & Smirnakis, S. M. Internal gain modulations, but not changes in stimulus contrast, preserve the neural code. J. Neurosci. 39, 1671–1687 (2019).

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Cortical gain is regulated by a wide range of influences, including attention, learning, locomotion, arousal and neuromodulatory activity, and these may act through a common set of cellular and circuit mechanisms.

    “Increasing evidence suggests that cortical gain is regulated by a wide range of influences, including attention, learning, locomotion, arousal and neuromodulatory activity, and that these may act through a common set of cellular and circuit mechanisms.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    GABAergic inhibition has a key but complex role in gain modulation, and sensitivity of individual neurons to sensory stimuli is profoundly modulated by changes in arousal state and locomotion.

    “Recent work highlights a key but complex role for GABAergic inhibition in gain modulation and suggests that the sensitivity of individual neurons to sensory stimuli is profoundly modulated by changes in arousal state and locomotion.”
    Quote in the paperSubstantial claim
34 of 34

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?

  • Head-restrained preparations have shown that behavioral state fluctuations have a strong impact on neuronal gain and coding properties in primary sensory areas and modulate neuronal firing within brain-wide neuronal networks.

    “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 [66]

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References 234 · Cited by 327

Authors
Ferguson, K. A., & Cardin, J. A.
Affiliations
Yale University (US)
Corresponding author
Cardin, Jessica A.
ORCID
Venue
Nature Reviews Neuroscience
Publisher
Springer Science and Business Media LLC
Published
2020-01-07
Volume
21
Issue
2
Pages
80-92
ISSN
  • 1471-003X (ISSN-L)
  • 1471-0048
DOI
10.1038/s41583-019-0253-y
PMC
PMC7408409
OpenAlex
W2999139721
Semantic Scholar
93bb14ab2f3b88e52cf7c48cb065f57394a7a03e
Cited by
327
References
234
Citations by year
2026: 46 · 2025: 52 · 2024: 69 · 2023: 43 · 2022: 48 · 2021: 49 · 2020: 19 · 2019: 1
FWCI
5.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature reviews. Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute of Mental Health (R01MH113852, R01MH102365)
  • National Eye Institute (K99EY030549, R01EY022951, P30EY026878)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
GABAergic inhibition, GABAergic interneurons, neuromodulation, membrane potential fluctuations, attention, arousal, perception, cognition, cortical circuits
MeSH terms
Animals, Arousal, Attention, Cerebral Cortex, Humans, Learning, Neurons, Perception

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