Complementary Ca2+ Activity of Sensory Activated and Suppressed Layer 6 Corticothalamic Neurons Reflects Behavioral State

Augustinaite, Sigita; Kühn, Bernd

doi:10.1016/j.cub.2020.07.069

Cited by 38 publications

(72 citation statements)

References 102 publications

(174 reference statements)

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“…L6CT pyramidal cells as targeted by the Ntsr1-Cre line 38, 50–52 are not homogeneous, but are known to contain at least two subtypes defined by morphology 51, 52, 123, 138, 139 , three subtypes defined by electrophysiology and morphology 139 , and four major subtypes defined by transcriptomics 138, 139 . For the mouse, it is currently unknown whether these subtypes differentially contribute to modulation by CT feedback.…”

Section: Discussionmentioning

confidence: 99%

Corticothalamic feedback sculpts visual spatial integration in mouse thalamus

Born

Erisken

Schneider

et al. 2020

Preprint

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20En route from retina to cortex, visual information travels through the dorsolateral geniculate nucleus of the thalamus (dLGN), where extensive cortico-thalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback components remains enigmatic. We show that in awake mice topographically organized cortical feedback modulates spatial integration in dLGN by sharpening receptive fields (RFs) and increasing surround suppression. Guided by a network model revealing wide-scale inhibitory CT feedback necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large receptive fields, show little surround suppression, and have strong feedback-dependent responses to large stimuli, making them an ideal candidate for mediating feedback-enhanced surround suppression in dLGN. We conclude that cortical feedback sculpts spatial integration in dLGN, likely via recruitment of neurons in visTRN. 21 24 object recognition 1, 2 , inference of depth and 3D structure from 2D images 3, 4 and semantic segmentation 5, 6 . Inspired by the 25 early visual system, these deep convolutional neural networks process visual information feedforward through a hierarchy of 26 layers. These layers each contain small computational units that, similar to real neurons operating within their receptive field 27 (RF), are applied to small patches of the image and during learning acquire selectivity for certain visual features. Remarkably, 28 such feature maps found in artificial networks resemble those of real neurons 7-9 , and the activity of various layers along the 29 artificial feedforward hierarchy can predict responses of real neurons in various cortical visual areas 2,[9][10][11] . Besides providing a 30 framework for machine vision, the feedforward architecture is also powerful for biological vision, where the initial feedforward 31 sweep contains a significant amount of information, sometimes sufficient to drive perception [12][13][14] . 32 So why then, is feedback such a prominent and ubiquitous motif in the brain, where descending projections generally 33 tend to outnumber ascending afferents? For instance, most inputs to primary sensory cortical areas do not come from primary 34 thalamus, but from higher-order structures 15 . The same principle applies to cortico-thalamic (CT) communication: relay cells 35 in the dorsolateral geniculate nucleus (dLGN) of the thalamus receive only 5-10% of their synaptic inputs from retinal afferents, 36 whereas 30% originate from L6 cortico-thalamic (L6CT) pyramidal cells of primary visual cortex (V1) 16 . 37Similar to a lack of general consensus regarding the function of cortico-cortical feedback 17, 18 , how CT feedback influences 38 the representation of visual information remains poorly understood. Despite massive cortical input, dLGN RFs closely resemble 39 retinal RFs rather than cortical ones [19][20...

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Section: Discussionmentioning

confidence: 99%

Corticothalamic feedback sculpts visual spatial integration in mouse thalamus

Born

Erisken

Schneider

et al. 2020

Preprint

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“…The independence of modulations by CT feedback and behavioral state is remarkable: neuromodulation accompanying locomotion also affects cortical layer 6, which receives dense cholinergic afferents from basal forebrain [128], and mouse V1 L6 CT neurons increase action potential firing in slice recordings upon bath application of ACh [129]. Potentially related, many V1 L6 CT neurons themselves increase activity during locomotion or arousal [85, 130]. While it is therefore unclear why such modulations of V1 L6 CT neurons only contribute relatively little to the dLGN locomotion effects, our result is similar to recent findings in superior colliculus (SC), where locomotion-related response modulations were also independent of V1 feedback [131].…”

Section: Discussionmentioning

confidence: 99%

Robust effects of corticothalamic feedback and behavioral state on movie responses in mouse dLGN

Špaček¹,

Born²,

Crombie³

et al. 2019

Preprint

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8Neurons in the dorsolateral geniculate nucleus (dLGN) of the thalamus are contacted by a large number of feedback synapses from cortex, whose role in visual processing is poorly understood. Past studies investigating this role have mostly used simple visual stimuli and anesthetized animals, but corticothalamic (CT) feedback might be particularly relevant during processing of complex visual stimuli, and its effects might depend on behavioral state. Here, we find that CT feedback robustly modulates responses to naturalistic movie clips by increasing response gain and promoting tonic firing mode. Compared to these robust effects for naturalistic movies, CT feedback effects were less consistent for simple grating stimuli. Finally, while CT feedback and locomotion affected dLGN responses in similar ways, we found their effects to be largely independent. We propose that CT feedback and behavioral state use separate routes to powerfully modulate visual information on its way to cortex.

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“…In particular, TRN neurons display an extended hyperpolarization in response to cholinergic input (McCormick, 1992;Sun et al, 2013), which in the TRN includes the BF, whose cortical projections have been linked to pupil dilation (Nelson & Mooney, 2016;Reimer et al, 2016). Second, the activity level of excitatory layer 6 corticothalamic feedback neurons (CTs) seems to increase with both to the presence of ACh (Sundberg et al, 2018) and pupil size (Augustinaite & Kuhn, 2020), and could thus contribute to promoting the dilation-associated tonic firing we observed here. Furthermore, at lower activity levels, CT feedback can have a suppressive effect on TCs (Crandall et al, 2015), meaning that a pupil-linked decrease in CT feedback (Augustinaite & Kuhn, 2020) could promote bursting.…”

Section: Discussionmentioning

confidence: 71%

“…Second, the activity level of excitatory layer 6 corticothalamic feedback neurons (CTs) seems to increase with both to the presence of ACh (Sundberg et al, 2018) and pupil size (Augustinaite & Kuhn, 2020), and could thus contribute to promoting the dilation-associated tonic firing we observed here. Furthermore, at lower activity levels, CT feedback can have a suppressive effect on TCs (Crandall et al, 2015), meaning that a pupil-linked decrease in CT feedback (Augustinaite & Kuhn, 2020) could promote bursting. On the other hand, related work from our lab has shown that during locomotion the reduction in thalamic bursting and overall increase in firing rates still occurs even when CT feedback is optogenetically suppressed (Spacek et al, 2020).…”

Section: Discussionmentioning

confidence: 76%

“…While raw pupil size has been successfully employed as a marker for internal arousal processes in sensory circuits (Augustinaite & Kuhn, 2020;Liang et al, 2020;Lin et al, 2019;McGinley et al, 2015a;Molnár et al, 2021;Petty et al, 2021;Schröder et al, 2020;Schwartz et al, 2020), other studies, including ours, have instead considered the pupil size signal as a continuous oscillatory process and focused on its dynamics (Reimer et al, 2014(Reimer et al, , 2016. We further extended this perspective by exploiting a decomposition method that respects the non-stationary, multi-scale nature of the pupil size signal (Huang et al, 1998), and found that several timescales were related to dLGN firing modes.…”

Section: Discussionmentioning

confidence: 93%

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Pupil Size Dynamics Predict dLGN Firing Mode Over a Wide Range of Timescales

Crombie

Špaček

Leibold³

et al. 2021

SSRN Journal

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Pupil size is a commonly used proxy for waking brain states such as arousal, and has been related to activity modulations in cortical sensory areas. Here, we asked whether the dorsolateral geniculate nucleus (dLGN), which provides sensory input to the visual cortex, is modulated by pupil-indexed arousal. Observing that the pupil size oscillates at multiple timescales, we developed a method to show that the spiking mode of the dLGN is predicted by pupil size oscillations over several of these timescales. Overall, we found that tonic spikes preferentially occurred during pupil dilation, while bursts occurred during contraction. These preferences could not be explained solely by pupil size per se or by the locomotion of the animal, and were also present during periods of stimulus viewing. We conclude that dLGN spiking activity is modulated by pupil-indexed arousal processes on various timescales, influencing the mode in which sensory signals are passed on to the cortex..

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Complementary Ca2+ Activity of Sensory Activated and Suppressed Layer 6 Corticothalamic Neurons Reflects Behavioral State

Cited by 38 publications

References 102 publications

Corticothalamic feedback sculpts visual spatial integration in mouse thalamus

Corticothalamic feedback sculpts visual spatial integration in mouse thalamus

Robust effects of corticothalamic feedback and behavioral state on movie responses in mouse dLGN

Pupil Size Dynamics Predict dLGN Firing Mode Over a Wide Range of Timescales

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