Look-up and look-down neurons in the mouse visual thalamus during freely moving exploration

Orlowska‐Feuer, Patrycja; Ebrahimi, Aghileh S.; Zippo, Antonio G.; Petersen, Rasmus S.; Lucas, Robert J.; Storchi, Riccardo

doi:10.1016/j.cub.2022.07.049

Cited by 13 publications

(8 citation statements)

References 67 publications

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“…S3A; see also [8,12]), eye movements (Fig. S4C; see also [87]), head movements [67], or particular postures in freely moving conditions [88],…”

Section: Discussionmentioning

confidence: 99%

“…S3A; see also [8, 12]), eye movements (Fig. S4C; see also [87]), head movements [67], or particular postures in freely moving conditions [88], modulate activity in the rodent early visual system. These modulations often occur in time-windows of seconds or less, which may be explained by the need to account for their immediate influence on sensory inputs [34, 89].…”

Section: Discussionmentioning

confidence: 99%

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Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales

Crombie

Spacek

Leibold

et al. 2021

Preprint

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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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“…S3A; see also [8,12]), eye movements (Fig. S4C; see also [87]), head movements [67], or particular postures in freely moving conditions [88],…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales

Crombie

Spacek

Leibold

et al. 2021

Preprint

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“…(2020) found 23% of mouse visual cortex neurons (∼60,000 total) did not reliably respond to visual stimuli, with the proportion of non-responsive units increasing in higher visual areas. They speculated that these units might be involved in specific natural features from hierarchical processing, modulated by multimodal senses (Stringer et al ., 2019), or non-visual computation, such as motion, identified in the dLGN of mice (Orlowska-Feuer et al ., 2022), which can all be true for our N units. It is also possible that these N units may explain the retinal ganglion cell classes projecting to the LGN that are not associated with classic thalamic responses (∼20%: Dacey, 2004).…”

Section: Discussionmentioning

confidence: 99%

More than expected: extracellular waveforms and functional responses in monkey LGN

Sun,

Killian,

Pezaris

2023

Preprint

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Unlike the exhaustive determination of the retina, key populations in the lateral geniculate nucleus of the thalamus (LGN) may have been missed. Here, we have begun to characterize the full range of extracellular neuronal responses in the LGN of awake monkeys using multi-electrodes during the presentation of colored noise visual stimuli to identify any previously overlooked signals. The extracellular spike waveforms of single units were classified into seven distinct classes: four negative-dominant classes that were narrow or broad, commonly associated with inhibitory or excitatory neurons, respectively; one triphasic class; and two positive-dominant classes. These units had their receptive fields (RF) mapped using spike-triggered averaging and were classified into magnocellular (M), parvocellular (P), koniocellular (K), and non-RF (N) neurons. Spike classes were correlated with their mapped RF and response characteristics to identify any relationships between spike shape and cell type: negative and narrow spiking waveform units had RFs that were mostlyP; negative and broad waveform units, mostlyN; and positive waveform units, mostlyM, as well as having shorter response latencies, larger RF sizes, and larger eccentricities than the other waveform classes.Ncells, those without an estimated RF, have not been regularly reported before. We observed that mostNcells consistently responded to the visually presented noise stimulus at a lower and more sustained rate than units with an RF. Our observations indicate that the population of LGN cells may be broader than traditionally held.Significance StatementThis study aims to extensively survey the extracellular space in the LGN of rhesus macaques. We found significant functional differences across extracellular waveform classes, such as their receptive field (RF) and spiking characteristics (e.g., firing rate, burstiness, latency). We also found a set of single units that did not yield an RF but reliably responded to the visually presented stimuli. Our findings reveal that the neural population of LGN may extend beyond the classical divisions of magnocellular, parvocellular, and koniocellular responses. This new finding will have interesting implications for our understanding of LGN function and interpretation of extracellular signals, especially significant with the recent advancement of dense multi-electrode arrays.

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“…Animals used to collect 2D data were not implanted. During transfer between the cage and the behavioural arena we used the tube handling procedure instead of tail picking, as prescribed in 36 , in order to minimise stress and reduce variability across animals. At the end of the experiment animals were terminated by neck dislocation.…”

Section: Methodsmentioning

confidence: 99%

Three-dimensional unsupervised probabilistic pose reconstruction (3D-UPPER) for freely moving animals

Ebrahimi

Orlowska‐Feuer

Huang

et al. 2023

Sci Rep

Self Cite

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A key step in understanding animal behaviour relies in the ability to quantify poses and movements. Methods to track body landmarks in 2D have made great progress over the last few years but accurate 3D reconstruction of freely moving animals still represents a challenge. To address this challenge here we develop the 3D-UPPER algorithm, which is fully automated, requires no a priori knowledge of the properties of the body and can also be applied to 2D data. We find that 3D-UPPER reduces by $$>10$$ > 10 fold the error in 3D reconstruction of mouse body during freely moving behaviour compared with the traditional triangulation of 2D data. To achieve that, 3D-UPPER performs an unsupervised estimation of a Statistical Shape Model (SSM) and uses this model to constrain the viable 3D coordinates. We show, by using simulated data, that our SSM estimator is robust even in datasets containing up to 50% of poses with outliers and/or missing data. In simulated and real data SSM estimation converges rapidly, capturing behaviourally relevant changes in body shape associated with exploratory behaviours (e.g. with rearing and changes in body orientation). Altogether 3D-UPPER represents a simple tool to minimise errors in 3D reconstruction while capturing meaningful behavioural parameters.

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Look-up and look-down neurons in the mouse visual thalamus during freely moving exploration

Cited by 13 publications

References 67 publications

Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales

Spiking activity in the visual thalamus is coupled to pupil dynamics across temporal scales

More than expected: extracellular waveforms and functional responses in monkey LGN

Three-dimensional unsupervised probabilistic pose reconstruction (3D-UPPER) for freely moving animals

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