Membrane potential correlates of sensory perception in mouse barrel cortex

Sachidhanandam, Shankar; Sreenivasan, Varun; Kyriakatos, Alexandros; Kremer, Yves; Petersen, Carl C. H.

doi:10.1038/nn.3532

Cited by 369 publications

(456 citation statements)

References 59 publications

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“…No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those reported in previous publications in the field 9,11 . To test for normality we used a Kolmogorov-Smirnov test.…”

Section: Discussionmentioning

confidence: 81%

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A somatosensory circuit for cooling perception in mice

et al. 2014

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The temperature of an object provides important somatosensory information for animals performing tactile tasks. Humans can perceive skin cooling of less than one degree, but the sensory afferents and central circuits they engage to enable the perception of surface temperature are poorly understood. To address these questions, we examined the perception of glabrous skin cooling in mice. We found that mice were also capable of perceiving small amplitude skin cooling and that primary somatosensory (S1) cortical neurons were required for cooling perception. Moreover, the absence of the menthol-gated transient receptor potential melastatin 8 ion channel in sensory afferent fibers eliminated the ability to perceive cold and the corresponding activation of S1 neurons. Our results identify parts of a neural circuit underlying cold perception in mice and provide a new model system for the analysis of thermal processing and perception and multimodal integration.An accurate sense of surface temperature helps animals to perceive object structure and identity. Psychophysical experiments have shown that humans are able to perceive tiny changes in skin cooling with a range between 0.4 and 1.8 ˚C 1,2 . It has, however, proved challenging to assess the perceptual ability of rodents to discriminate small temperature steps at threshold levels.Classical paw withdrawal tests cannot differentiate between reflexive avoidance behavior and sensory perception 3 . Two-plate thermal preference arenas have shown that mice avoid cooler floor temperatures [4][5][6] , but this test has limited spatial and temporal control of the stimulus and lacks fine-grained resolution for near threshold perception. We therefore developed a shortlatency, goal-directed thermal perception task using the glabrous skin of the mouse forepaw.A general dogma is that all somatosensory input, including thermal, is integrated by the primary somatosensory cortex (S1) to form a coherent sensory percept. S1 is necessary for tactile somatosensory perception in rodents [7][8][9][10][11][12][13] . The role of S1 in thermal perception, however, is under debate, 3 with three studies concluding that rodent S1 is not involved [14][15][16] and another concluding that it is 17 . This may be because these studies used large cortical lesions with long recovery and retraining periods in freely moving rats that used facial regions to detect temperature [14][15][16][17] .Likewise, very little is known about the underlying cortical neural processing of non-noxious thermal stimuli in rodents. To the best of our knowledge, only one study, conducted in anesthetized rats stimulating scrotal skin, has shown extracellular responses of cortical neurons to thermal stimulation 18 . At the sensory periphery, a range of primary afferents including myelinated Aβ mechanoreceptors 2,19 , thinly myelinated Aδ-fibers and unmyelinated polymodal C fibers, fire during skin cooling 4,20,21 . Although it is thought that thickly myelinated Aδ fibers are responsible for cooling perception, C fibers ...

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Section: Discussionmentioning

confidence: 81%

“…S1 is necessary for tactile somatosensory perception in rodents [7][8][9][10][11][12][13] . The role of S1 in thermal perception, however, is under debate, 3 with three studies concluding that rodent S1 is not involved [14][15][16] and another concluding that it is 17 .…”

Section: Introductionmentioning

confidence: 99%

A somatosensory circuit for cooling perception in mice

et al. 2014

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“…Global dynamics resembling up and down switching have been observed in rodents during quiescent wakefulness (43,44) or during a perceptual task (45) as well as in awake primates (46). Previous studies have hypothesized that correlations could impact the encoding of information in large networks.…”

Section: Discussionmentioning

confidence: 99%

“…Our results contribute to build a mechanistic framework for recent findings showing that, depending on variables such as sleep pressure, task engagement, locomotion, or sensory stimulation, circuits exhibit different dynamics that shape the structure of correlations. Whether these correlations ultimately impact information encoding will depend on how efficiently animals process sensory information under these different brain states (45,47), a question that needs to be further investigated.…”

Section: Discussionmentioning

confidence: 99%

Stochastic transitions into silence cause noise correlations in cortical circuits

Mochol

Hermoso-Mendizabal

Sakata

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.neuronal variability | noise correlations | brain state | auditory cortex | stochastic network dynamics N euronal noise correlations are defined as common fluctuations in the spiking activity of neurons under conditions of constant sensory input or motor output. Traditionally, they have been thought to arise from the dense connectivity of the cortex, such that neighboring neurons sharing a fraction of their inputs should also share a fraction of their output variability (1). Several observations are consistent with this hypothesis: pairwise correlations in the cortex decrease with cell pair distance (2) or with the difference in stimulus selectivity (3), dependencies that could follow from a variation in shared input given the anatomy of cortical circuits. Recent findings, however, challenge this simple interpretation. Recordings in the primate visual cortex have shown that attention or task context can change correlation structure (4-6) and that the magnitude of averaged correlation can be very low (7). In anesthetized rodents correlations decrease with brain state desynchronization (8, 9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11-13) seems to occur jointly with a transient decrease in correlation (2, 14, 15). Th...

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“…Results are plotted as mean ± SEM over n = 43 stimulus pairs. been shown to be particularly relevant for behavior (18). Across the 125-to 200-ms late-response period, the gain of information of the denoised responses over the information present in the original MUA was in the range of 32-71% for the true-LC model and 18-39% for the no-LC model.…”

Section: Extracting Sensory Information From State-dependent Corticalmentioning

confidence: 99%

Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

Safaai

Neves

Eschenko

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Neuronal responses to sensory stimuli are not only driven by feedforward sensory pathways but also depend upon intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation. To understand how these factors together regulate cortical dynamics, we recorded simultaneously spontaneous and somatosensory-evoked multiunit activity from primary somatosensory cortex and from the locus coeruleus (LC) (the neuromodulatory nucleus releasing norepinephrine) in urethane-anesthetized rats. We found that bursts of ipsilateral-LC firing preceded by few tens of milliseconds increases of cortical excitability, and that the 1-to 10-Hz rhythmicity of LC discharge appeared to increase the power of deltaband (1-4 Hz) cortical synchronization. To investigate quantitatively how LC firing might causally influence spontaneous and stimulus-driven cortical dynamics, we then constructed and fitted to these data a model describing the dynamical interaction of stimulus drive, ongoing synchronized cortical activity, and noradrenergic neuromodulation. The model proposes a coupling between LC and cortex that can amplify delta-range cortical fluctuations, and shows how suitably timed phasic LC bursts can lead to enhanced cortical responses to weaker stimuli and increased temporal precision of cortical stimulus-evoked responses. Thus, the temporal structure of noradrenergic modulation may selectively and dynamically enhance or attenuate cortical responses to stimuli. Finally, using the model prediction of single-trial cortical stimulus-evoked responses to discount single-trial state-dependent variability increased by ∼70% the sensory information extracted from cortical responses. This suggests that downstream circuits may extract information more effectively after estimating the state of the circuit transmitting the sensory message.oscillations | state dependence | dynamical systems | information coding | somatosensation R esponsiveness of cortical sensory neurons is state dependent. In other words, the neural responses to a sensory stimulus do not only depend on the features of extrinsic sensory inputs but also on intrinsic network variables that can be collectively defined as the "network state" (1). Cortical sensory neurons receive information about the external world from peripheral receptors via feedforward sensory pathways. However, the abundance of recurrent and feedback connectivity (2) may generate ongoing activity that shapes the background on which the afferent information is processed (3). In addition, neuromodulatory inputs from neurochemically specialized brain nuclei that are not part of the direct spino-thalamo-cortical pathway can modulate the dynamics of cortical networks (4), as well as control the animal's behavioral state. The concurrent integration of information about the external world and about internal states is likely to be central for computational operations of cortical circuits and for the production of complex behavior, yet its mechanisms and implications for...

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Membrane potential correlates of sensory perception in mouse barrel cortex

Cited by 369 publications

References 59 publications

A somatosensory circuit for cooling perception in mice

A somatosensory circuit for cooling perception in mice

Stochastic transitions into silence cause noise correlations in cortical circuits

Modeling the effect of locus coeruleus firing on cortical state dynamics and single-trial sensory processing

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