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During navigation, rodents continually sample the environment with their whiskers. How locomotion modulates neuronal activity in somatosensory cortex, and how it is integrated with whisker-touch remains unclear. Here, we compared neuronal activity in layer 2/3 (L2/3) and L5 of barrel cortex using calcium imaging in mice running in a tactile virtual reality. Both layers increase their activity during running and concomitant whisking, in the absence of touch. Fewer neurons are modulated by whisking alone. Whereas L5 neurons respond transiently to wall-touch during running, L2/3 neurons show sustained activity. Consistently, neurons encoding running-with-touch are more abundant in L2/3 and they encode the run-speed better during touch. Few neurons across layers were also sensitive to abrupt perturbations of tactile flow during running. In summary, locomotion significantly enhances barrel cortex activity across layers with L5 neurons mainly reporting changes in touch conditions and L2/3 neurons continually integrating tactile stimuli with running.

Successful navigation through the world requires accurate estimation of one's own speed. To derive this estimate, animals integrate visual speed gauged from optic flow and run speed gauged from proprioceptive and locomotor systems. The primary visual cortex (V1) carries signals related to visual speed, and its responses are also affected by run speed. To study how V1 combines these signals during navigation, we recorded from mice that traversed a virtual environment. Nearly half of the V1 neurons were reliably driven by combinations of visual speed and run speed. These neurons performed a weighted sum of the two speeds. The weights were diverse across neurons, and typically positive. As a population, V1 neurons predicted a linear combination of visual and run speeds better than either visual or run speeds alone. These data indicate that V1 in the mouse participates in a multimodal processing system that integrates visual motion and locomotion during navigation.

Growing evidence indicates that responses in sensory cortex are modulated by factors beyond direct sensory stimulation. In primary visual cortex (V1), for instance, responses increase with locomotion. Here we show that this increase is accompanied by a profound change in spatial integration. We recorded from V1 neurons in head-fixed mice placed on a spherical treadmill. We characterized spatial integration and found that the responses of most neurons were suppressed by large stimuli. As in primates, this surround suppression increased with stimulus contrast. These effects were captured by a divisive normalization model, where the numerator originates from a central region driving the neuron and the denominator originates from a larger suppressive field. We then studied the effects of locomotion and found that it markedly reduced surround suppression, allowing V1 neurons to integrate over larger regions of visual space. Locomotion had two main effects: it increased spontaneous activity, and it weakened the suppressive signals mediating normalization, relative to the driving signals. We conclude that a fundamental aspect of visual processing, spatial integration, is controlled by an apparently unrelated factor, locomotion. This control might operate through the mechanisms that are in place to deliver surround suppression., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Locomotion produces full-field optic flow that often dominates the visual motion inputs to an observer. The perception of optic flow is in turn important for animals to guide their heading and interact with moving objects. Understanding how locomotion influences optic flow processing and perception is therefore essential to understand how animals successfully interact with their environment. Here, we review research investigating how perception and neural encoding of optic flow are altered during self-motion, focusing on locomotion. Self-motion has been found to influence estimation and sensitivity for optic flow speed and direction. Nonvisual self-motion signals also increase compensation for self-driven optic flow when parsing the visual motion of moving objects. The integration of visual and nonvisual self-motion signals largely follows principles of Bayesian inference and can improve the precision and accuracy of self-motion perception. The calibration of visual and nonvisual self-motion signals is dynamic, reflecting the changing visuomotor contingencies across different environmental contexts. Throughout this review, we consider experimental research using humans, non-human primates and mice. We highlight experimental challenges and opportunities afforded by each of these species and draw parallels between experimental findings. These findings reveal a profound influence of locomotion on optic flow processing and perception across species. This article is part of a discussion meeting issue 'New approaches to 3D vision'. [ABSTRACT FROM AUTHOR]

A substantia] portion of our sensory experience happens during active behaviors such as walking around or paying attention. How do sensory systems work during such behaviors? Neural processing in sensory systems can be shaped by behavior in multiple ways ranging from a modulation of responsiveness or sharpening of tuning to a dynamic change of response properties or functional connectivity. Here, we review recent findings on the modulation of sensory processing during active behaviors in different systems: insect vision, rodent thalamus, and rodent sensory cortices. We discuss the circuit-level mechanisms that might lead to these modulations and their potential role in sensory function. Finally, we highlight the open questions and future perspectives of this exciting new field. [ABSTRACT FROM AUTHOR]

This Matters Arising Response contains our commentary to the response written by Vasilevskaya et al., 2023, publishing concurrently in Cell Reports , for our recent article "Feature selectivity can explain mismatch signals in mouse visual cortex." We find that results in the response reinforced many of our findings and, further supported by their new results, we argue for the necessity to redefine sensorimotor mismatch selectivity in the mouse visual system. • Visual flow perturbation responses in open-loop can be larger when animals run • Sensorimotor mismatch selectivity should be redefined accounting for these responses • Previous results on sensorimotor mismatch need to be interpreted with caution In this Matters Arising Response, Muzzu and Saleem argue that the new results of the Matters Arising by Vasilevkaya et al. support many of their original findings. They reiterate the need to redefine sensorimotor mismatch selectivity in the mouse visual system. [ABSTRACT FROM AUTHOR]

Sensory experience often depends on one's own actions, including self-motion. Theories of predictive coding postulate that actions are regulated by calculating prediction error, which is the difference between sensory experience and expectation based on self-generated actions. Signals consistent with prediction error have been reported in the mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show that such signals can be elicited by visual stimuli uncoupled to an animal running. We record V1 neurons while presenting drifting gratings that unexpectedly stop. We find strong responses to visual perturbations, which are enhanced during running. Perturbation responses are strongest in the preferred orientation of individual neurons, and perturbation-responsive neurons are more likely to prefer slow visual speeds. Our results indicate that prediction error signals can be explained by the convergence of known motor and sensory signals, providing a purely sensory and motor explanation for purported mismatch signals. [Display omitted] • Mouse primary visual cortex neurons respond to perturbations of visual flow • Perturbation responses are enhanced by running • Perturbation responses are stronger in the preferred orientation of the neurons • Perturbation-responsive neurons are tuned to slow visual speeds Muzzu and Saleem find neurons in the mouse visual cortex responding to stops or perturbations of open-loop visual flow. Perturbation responses are enhanced by running, and responsive neurons prefer low visual speeds. These results suggest concurrent selectivity for low visual speeds and running can explain sensorimotor mismatch signals in the visual cortex. [ABSTRACT FROM AUTHOR]