Mechanisms underlying a thalamocortical transformation during active tactile sensation

Gutnisky, Diego; Yu, Jianing; Hires, Samuel Andrew; To, Minh‐Son; Bale, Michael R.; Svoboda, Karel; Golomb, David

doi:10.1371/journal.pcbi.1005576

Cited by 45 publications

(54 citation statements)

References 95 publications

(199 reference statements)

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“…On the other hand, optogenetic perturbation studies that allow direct tests of the ‘paradoxical effect’ prediction have produced contradictory results. While some paradoxical changes in inhibitory currents have been observed [17; 18], several studies have found non-paradoxical effects in inhibitory rate or currents [18; 19; 20] or found disinhibitory circuits that could produce paradoxical effects in a non-ISN [18]. Possible explanations for these variations include differences between anesthetized and awake states, differences between measurements of firing rates and intracellular currents (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inhibition stabilization is a widespread property of cortical networks

Sanzeni

Akitake

Goldbach

et al. 2019

Preprint

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Many cortical network models use recurrent coupling strong enough to require inhibition for stabilization. Yet it has been experimentally unclear whether inhibition-stabilized network (ISN) models describe cortical function well across areas and states. Here we test several ISN predictions, including the counterintuitive (paradoxical) suppression of inhibitory firing in response to optogenetic inhibitory stimulation. We find clear evidence for ISN operation in mouse visual, somatosensory, and motor cortex. Simple two-population ISN models describe the data well and let us quantify coupling strength. Though some models predict a non-ISN to ISN transition with increasingly strong sensory stimuli, we find ISN effects without sensory stimulation and even during light anesthesia. Additionally, average paradoxical effects result only with transgenic, not viral, opsin expression in parvalbumin (PV)-positive neurons; theory and expression data show this is consistent with ISN operation. Taken together, these results show strong coupling and inhibition stabilization are common features of cortex.[149 words] I. INTRODUCTION 16Extensive recurrent connectivity between nearby neurons is an ubiquitous feature of the cerebral cortex [1; 17 2; 3; 4]. Theoretical work has shown that the strength of recurrent coupling has a major impact on several 18 computational properties of networks of excitatory (E) and inhibitory (I) neurons, including the speed of 19 the response to external stimuli [5; 6], the ability of a network to sustain persistent activity [7], the capacity 20 and robustness of memory storage [8], and the amplification of various input modes [9]. 21 Strong excitatory recurrent coupling, however, can lead to unstable dynamics unless stabilized by inhi-22 bition. When recurrent connectivity is weak, excitatory cells can show stable firing rates independent of the 23 activity of inhibitory cells. In networks with strong recurrent connections, excitatory-to-excitatory (E-E) 24 connections amplify responses so that the excitatory network is unstable if the firing rates of inhibitory 25 neurons are kept fixed. Stable excitatory network operation across a range of firing rates can be restored 26 2 if inhibitory recurrent connections are sufficiently strong, allowing inhibition to track and balance excita-27 tion [5; 6; 7; 10; 11]. Such network models, with strong recurrent connections rendering the excitatory 28 cells self-amplifying and thus unstable, and requiring inhibition for stability, are called inhibitory-stabilized 29 networks (ISNs) [12]. 30 Whether cortical networks function in the ISN regime, in which conditions they do so, and which cortical 31 areas may operate as ISNs has been the subject of debate. Cat primary visual cortex (V1) shows behavior 32 consistent with the ISN regime [12]. But since that work used sensory stimuli, it could not determine whether 33 cat V1 operates as an ISN in the absence of visual stimuli (i.e. at rest). Based on these data and others, 34 Miller and co-authors later d...

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Section: Introductionmentioning

confidence: 99%

“…Possible explanations for these variations include differences between anesthetized and awake states, differences between measurements of firing rates and intracellular currents (e.g. due to ‘space clamp’ effects), or differences in which or how many inhibitory cells are stimulated [20; 21; 22].…”

Section: Introductionmentioning

confidence: 99%

Inhibition stabilization is a widespread property of cortical networks

Sanzeni

Akitake

Goldbach

et al. 2019

Preprint

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“…Tactile suppression is likely to be mediated at multiple levels within sensorimotor systems, and may be effected by corollary motor commands and proprioceptive reafference 3,8,10,[26][27][28][29][30] . Here, we find that activation of M1 NTSR1+ layer VI cortico-thalamic neurons induces an excitationsuppression sequence across thalamocotical circuits, suggesting that cortico-thalamic projections from M1 to somatosensory thalamus are sufficient to induce some aspects of tactile suppression.…”

Section: Discussionmentioning

confidence: 99%

Motor cortex can modulate somatosensory processing via cortico-thalamo-cortical pathway

Lohse

Cooper

Sader

et al. 2018

Preprint

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The somatosensory and motor systems are intricately linked, providing several routes for the sensorimotor interactions necessary for haptic processing. Here, we used electrical and optogenetic stimulation to study the circuits that enable primary motor cortex (M1) to exert top-down modulation of whisker-evoked responses, at the levels of brain stem, thalamus and somatosensory cortex (S1).We find that activation of M1 drives somatosensory responsive cells at all levels, and that this excitation is followed by a period of tactile suppression, which gradually increases in strength along the ascending somatosensory pathway. Using optogenetic stimulation in the layer-specific Cre driver lines, we find that activation of layer VI cortico-thalamic neurons is sufficient to drive spiking in higher order thalamus, and that this is reliably followed by excitation of S1, suggesting a cross-modal cortico-thalamo-cortical pathway. Cortico-thalamic excitation predicts the degree of subsequent tactile suppression, consistent with a strong role for thalamic circuits in the expression of inhibitory sensorimotor interactions. These results provide evidence of a role for M1 in dynamic modulation of S1, largely under cortico-thalamic control.

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“…torque generated as contraction of the whisking muscles cause the whiskers to bend against the object (Campagner et al, 2016;Severson et al, 2017;Bush et al, 2016;reviewed by Campagner et al, 2017). PWNs robustly encode both the direction and magnitude of bending and transmit this information along the ascending thalamo-cortical pathway (Yu et al, 2006(Yu et al, , 2016O'Connor et al, 2010b;Huber et al, 2012;Petreanu et al, 2012;Xu et al, 2012;Hires et al, 2015;Moore et al, 2015;Peron et al, 2015b;Gutnisky et al, 2017). A wide range of PWN properties (Zucker and Welker, 1969;Gibson and Welker, 1983b;Lichtenstein et al, 1990;Szwed et al, 2003;Jones et al, 2004;Arabzadeh et al, 2005;Leiser and Moxon, 2007;Bale and Petersen, 2009;Lottem and Azouz, 2011;Bale et al, 2013;Maravall et al, 2013) can be concisely explained by this framework (Campagner et al, 2017).…”

Section: Mechanosensory Basis Of Active Touchmentioning

confidence: 99%

Prediction of Choice From Competing Mechanosensory and Choice-Memory Cues During Active Tactile Decision Making

Campagner

Evans

Chlebikova

et al. 2018

Preprint

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Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Mice were trained to localise a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision we estimated whisker-object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosenory and choice-memory signals; and provides a new task, well-suited for future study of the neural basis of active perceptual decisions.

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Mechanisms underlying a thalamocortical transformation during active tactile sensation

Cited by 45 publications

References 95 publications

Inhibition stabilization is a widespread property of cortical networks

Inhibition stabilization is a widespread property of cortical networks

Motor cortex can modulate somatosensory processing via cortico-thalamo-cortical pathway

Prediction of Choice From Competing Mechanosensory and Choice-Memory Cues During Active Tactile Decision Making

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