Body ownership disorders can be triggered by disease or body damage. Methods to probe limb embodiment are required to address those disorders. This includes the development of neuroprostheses that better integrate into the body scheme of the user. To this end, the "rubber hand illusion" protocol is a key behavioral method to probe the powerful embodiment that can be triggered by congruent somatosensory and visual inputs from the limb. So far, the neurophysiology of limb embodiment remains poorly known, in part because translating the rubber hand illusion to animal models such as the mouse remains challenging. Yet, mapping out the brain circuits of embodiment thanks to the use of genetic and optogenetic research tools would allow to propose novel embodiment restoration strategies. Here, we show that the rubber hand illusion described in humans can be translated to the mouse forelimb model using an automated, videography-based procedure. We exposed head-fixed mice to a visible, static 3D-printed replica of the right forelimb, while their own forelimb was hidden from their sight. We synchronously brushed their hidden forelimb and the replica. Following these visuo-tactile associations, the replica was visually threatened, and we probed the reaction of the mice using automated tracking of pupils and facial expression. The mice focused significantly more of their gaze toward the threatened forelimb replica after receiving synchronous tactile and visual information compared to asynchronous. More generally, across test and control conditions, the mouse pupillary response was consistent with the human overt response to the rubber hand illusion. Thus, our results show that mice exhibit quantifiable behavioral markers of the embodiment of an artificial forelimb.

Robotic upper-limb prostheses aim to restore the autonomy of paralyzed patients and amputees. So far, advances in this field have relied on monkey pre-clinical and human clinical research. Here, we report on the direct brain control by mice of a miniature mouse forelimb prosthesis. We show that mice implanted with a cortical, microelectrode-based brain-machine interface can learn to control the prosthesis via neuronal operant conditioning, and solve a water collection task in a 2-dimensional and up to a 3-dimensional space. As they learned this task, the mice shaped increasingly consistent prosthesis movements that led to rewards, thanks to coordinated patterns of neuronal activity across the several control dimensions. Beyond the demonstration of unexpected cognitive and motor control abilities in mice, we anticipate that this preclinical model of upper-limb prosthesis control will be a tool to address several of the most pressing issues in prosthetics controlled by brain-machine interfaces. ### Competing Interest Statement The authors have declared no competing interest. CNRS, 80|Prime, PRIME interdisciplinary label Fondation pour la Recherche Médicale, https://ror.org/04w6kn183 La Fondation Dassault Systèmes Agence Nationale de la Recherche, JCJC Mesobrain, PRC Expect, PRC Motorsense, PRC Hermin Université Paris-Saclay, Lidex NeuroSaclay, Idex Brainscopes, iCODE, hCODE.

Tactile representations in the barrel field of the primary somatosensory cortex of rodents (wS1) receive inputs from two distinct thalamic nuclei, the ventro-posterior-medial nucleus (VPM) and the posterior medial complex (POm). Previous work has revealed a sweep-stick code in rat wS1 by using a novel whisker velocity-white noise stimulus. Sticks refer to high velocity single whisker bumps, while sweeps correspond to large multiwhisker displacements with extended temporal profiles. We hypothesized that barrel cortex neurons inherit ‘stick’ responses from the VPM and ‘sweep’ responses from the POm. Here we test this hypothesis by studying the coding strategy of both thalamic nuclei and wS1 in mice. We found a stratified wS1 representation of both sweep and stick functional classes, whereas VPM and POm contained mainly stick encoding neurons. Cortical layer 4 ‘stick’ responses are a delayed version from VPM, while layer 5b ‘sweep’ responses come from POm. Notably, layer 5a ‘sweep’ responses result from a temporal integration of ‘stick’ information from VPM and POm. Our results put forward a circuit scheme in which fast encoding stick events from VPM allow a fine-tuned texture processing in the cortex, modulated by sweep-responding cells that integrate multi-whisker information. ### Competing Interest Statement The authors have declared no competing interest. Fondation pour la Recherche Médicale, DEQ20170336761 European Union’s Horizon 2020, Marie Sklodowska-Curie grant agreement No 702726 Fondation de France, https://ror.org/02zkxjz73, 00120244 / WB-2021-35062 Agence Nationale de la Recherche, https://ror.org/00rbzpz17, ANR-23-CE37-0004-01 HiDeepID, ANR-22-CE37-0016-01 PerBaCo Conseil Régional d’île-de-France, https://ror.org/04ry1rt27, DIM C-BRAINS

Recent studies suggest that mesoscale cortical activity patterns encode movement-related information, such as direction and speed. We developed an approach to decode forelimb movement direction from mesoscale cortical dynamics in mice using wide-field GCaMP6f imaging. By simultaneously recording cortical activity and 3D kinematics of specific body points, we built a dataset linking 450 ms windows of cortical activity to movement categories (forward, backward, still). A convolutional neural network (CNN) trained on this dataset achieved real-time decoding at 100 Hz and sub- 10 ms latency, with a correlation of $\mathrm{r}=0.72$ between predicted and ground truth values. Although separate models were trained per animal, output distributions were highly consistent (mean inter-animal $r=0.91$), suggesting that mesoscale cortical dynamics related to movement are consistent across individuals. These results demonstrate the feasibility of the online decoding of movement types from wide-field cortical signals, towards minimally invasive upper limb neuroprosthetic.

Having the ability to probe the strength of limb embodiment is a requirement to better understand body ownership disorders that are triggered both by disease and by accidental body damage. It is also an essential tool towards the development of neuro-prostheses that better integrate into the user’s body representation.One key way to probe limb embodiment is through the rubber hand illusion. Here we adapted this paradigm to the mouse forelimb, which is a relevant model for upper limb research thanks to its diverse and rich behavioural characteristics and unparalleled access to genetic and optogenetic research tools.We exposed head-fixed mice to a visible, static 3D printed replica of their right forelimb, while their own forelimb was removed from their sight and stimulated by brush strokes in synchrony with the replica. Following these visuo-tactile stimulations, the replica was visually threatened, and we probed the mice’s reaction using automated tracking of pupils and facial expression. We found that mice focused significantly more their gaze towards the forelimb replica when they received congruent tactile and visual information, compared to control conditions. This observation is consistent with the human overt response to the rubber hand illusion. In summary, these findings indicate that mice can experience forelimb embodiment, and this phenomenon can be evaluated with the method we developed.### Competing Interest StatementThe authors have declared no competing interest.

At the surface of the cerebral cortex, activity dynamics measured at a mesoscopic (about 0.1 to 1 mm in mouse cortex) scale are characterized by both spontaneous and behavior-related dynamical waves of synchronized neuronal activity. These waves are thought to participate in information propagation and processing, but remain poorly understood. To assess if such mesoscopic coordinated neuronal dynamics can be controlled in a goal-directed manner, we implemented a task in which mice are trained to generate waves with specific trajectories in order to obtain rewards. We tracked propagating waves at the surface of the somatosensory-motor cortex of head-fixed mice in real time by means of wide field calcium imaging, and conditioned the delivery of rewards to the detection of wave trajectories responding to specific spatiotemporal criteria. We found that the majority of the trained mice significantly increased their performance, mainly by increasing the frequency of rewardable waves. As the mice learned to achieve this task, we observed changes in the spatiotemporal patterns of the cortical waves. By revealing that, upon learning, neuronal activity can be shaped at the mesoscopic scale to generate specific waves patterns, our work opens up new perspectives for brain-machine interfacing. ### Competing Interest Statement The authors have declared no competing interest.

Significance: The study of neuronal processes governing behavior in awake behaving mice is constantly boosted by the development of technological strategies, such as miniaturized microscopes and closed-loop virtual reality systems. However, the former limits the quality of recorded signals due to constrains in size and weight and the latter suffers from the restriction of the movement repertoire of the animal, therefore, hardly reproducing the complexity of natural multisensory scenes. Aim: Another strategy that takes advantage of both approaches consists of the use of a fiberbundle interface to carry optical signals from a moving animal to a conventional imaging system. However, as the bundle is usually fixed below the optics, its torsion resulting from rotations of the animal inevitably constrains the behavior over long recordings. Our aim was to overcome this major limitation of fibroscopic imaging. Approach: We developed a motorized optical rotary joint controlled by an inertial measurement unit at the animal's head. Results: We show its principle of operation, demonstrate its efficacy in a locomotion task, and propose several modes of operation for a wide range of experimental designs. Conclusions: Combined with an optical rotary joint, fibroscopic approaches represent an outstanding tool to link neuronal activity with behavior in mice at the millisecond timescale.

Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal spatiotemporal properties of the sensorimotor cortical integration that set constraints on the design of neuroprosthetics.

Objective.Distributed microstimulations at the cortical surface can efficiently deliver feedback to a subject during the manipulation of a prosthesis through a brain-machine interface (BMI). Such feedback can convey vast amounts of information to the prosthesis user and may be key to obtain an accurate control and embodiment of the prosthesis. However, so far little is known of the physiological constraints on the decoding of such patterns. Here, we aimed to test a rotary optogenetic feedback that was designed to encode efficiently the 360° movements of the robotic actuators used in prosthetics. We sought to assess its use by mice that controlled a prosthesis joint through a closed-loop BMI.Approach.We tested the ability of mice to optimize the trajectory of a virtual prosthesis joint in order to solve a rewarded reaching task. They could control the speed of the joint by modulating the activity of individual neurons in the primary motor cortex. During the task, the patterned optogenetic stimulation projected on the primary somatosensory cortex continuously delivered information to the mouse about the position of the joint.Main results.We showed that mice are able to exploit the continuous, rotating cortical feedback in the active behaving context of the task. Mice achieved better control than in the absence of feedback by detecting reward opportunities more often, and also by moving the joint faster towards the reward angular zone, and by maintaining it longer in the reward zone. Mice controlling acceleration rather than speed of the joint failed to improve motor control.Significance.These findings suggest that in the context of a closed-loop BMI, distributed cortical feedback with optimized shapes and topology can be exploited to control movement. Our study has direct applications on the closed-loop control of rotary joints that are frequently encountered in robotic prostheses.

The topographic organization is a prominent feature of sensory cortices, but its functional role remains controversial. Particularly, it is not well determined how integration of activity within a cortical area depends on its topography during sensory-guided behavior. Here, we train mice expressing channelrhodopsin in excitatory neurons to track a photostimulation bar that rotated smoothly over the topographic whisker representation of the primary somatosensory cortex. Mice learn to discriminate angular positions of the light bar to obtain a reward. They fail not only when the spatiotemporal continuity of the photostimulation is disrupted in this area but also when cortical areas displaying map discontinuities, such as the trunk and legs, or areas without topographic map, such as the posterior parietal cortex, are photostimulated. In contrast, when cortical topographic continuity enables to predict future sensory activation, mice demonstrate anticipation of reward availability. These findings could be helpful for optimizing feedback while designing cortical neuroprostheses.

The ability of the mammalian central nervous system to constantly adapt motor commands and optimise behaviour according to the context does not only rely on efficient analysis of the incoming sensory flow. The cerebral cortex is indeed thought to compute a dynamic model of our environment allowing anticipation of future sensory inputs. Such computation would imply the production of error signals in case of divergence between actual and predicted sensory inputs. Here, we aim to study the neuronal mechanisms at play in such sensory predictions using the tactile whisker system of mice as a model. To this end, we use a whisker-guided locomotion task where mice have to avoid two obstacles in their path. We wish to analyze the cortical dynamics evoked by the contact of the vibrissae with the obstacles in such familiar environment, but also to reveal the capacity of the somatosensory cortex to produce error signals when this environment is altered by surprise, either by the removal or change in position of an obstacle on the animals path. To do so, we developed an optical apparatus based on an image guide and various sensors. It allows us to record mesoscale voltage induced fluorescence changes over the primary somatosensory cortex at a subcolumnar resolution, while the animals are freely navigating and gathering tactile information in an ethologically relevant manner. By simultaneously recording the animals path adaptations and whisker movements, we will be able to correlate dynamics of cortical activity, sensory inputs, and motor adjustments, at the millisecond timescale.

The topographic organization of sensory cortices is a prominent feature, but its functional role remains unclear. Particularly, how activity is integrated within a cortical area depending on its topography is unknown. Here, we trained mice expressing channelrhodopsin in cortical excitatory neurons to track a bar photostimulation that rotated smoothly over the primary somatosensory cortex (S1). When photostimulation was aimed at vS1, the area which contains a contiguous representation of the whisker array at the periphery, mice could learn to discriminate angular positions of the bar to obtain a reward. In contrast, they could not learn the task when the photostimulation was aimed at the representation of the trunk and legs in S1, where neighboring zones represent distant peripheral body parts, introducing discontinuities. Mice demonstrated anticipation of reward availability, specifically when cortical topography enabled to predict future sensory activation. These results are particularly helpful for designing efficient cortical sensory neuroprostheses. Teaser Optogenetic stimulation sweeping the cortical surface: A way to provide precise sensory information and guide behaviour.

In rat barrel cortex, feature encoding schemes uncovered during broadband whisker stimulation are hard to reconcile with the simple stick-slip code observed during natural tactile behaviors, and this has hindered the development of a generalized computational framework. By designing broadband artificial stimuli to sample the inputs encoded under natural conditions, we resolve this disparity while markedly increasing the percentage of deep layer neurons found to encode whisker movements, as well as the diversity of these encoded features. Deep layer neurons encode two main types of events, sticks and sweeps, corresponding to high angular velocity bumps and large angular displacements with high velocity, respectively. Neurons can exclusively encode sticks or sweeps, or they can encode both, with or without direction selectivity. Beyond unifying coding theories from naturalistic and artificial stimulation studies, these findings delineate a simple and generalizable set of whisker movement features that can support a range of perceptual processes.

The representation of rodents’ mystacial vibrissae within the primary somatosensory (S1) cortex has become a major model for studying the cortical processing of tactile sensory information. However, upon vibrissal stimulation, tactile information first reaches S1 but also, almost simultaneously, the secondary somatosensory cortex (S2). To further understand the role of S2 in the processing of whisker inputs, it is essential to characterize the spatio-temporal properties of whisker-evoked response dynamics in this area. Here we describe the topography of the whiskers representation in the mouse S2 with voltage sensitive dye imaging. Analysis of the spatial properties of the early S2 responses induced by stimulating individually 22 to 24 whiskers revealed that they are spatially ordered in a mirror symmetric map with respect to S1 responses. Evoked signals in S2 and S1 are of similar amplitude and closely correlated at the single trial level. They confirm a short delay (~3 ms) between S1 and S2 early activation. In both S1 and S2 caudo-dorsal whiskers induce stronger responses than rostro-ventral ones. Finally, analysis of early C2-evoked responses indicates a faster activation of neighboring whisker representations in S2 relative to S1, probably due to the reduced size of the whisker map in S2.

Closed-loop brain-machine interfaces may help restore the autonomy of amputees and tetraplegic patients. However, additional efforts are needed towards their real-world use with prostheses. Here we have interfaced a highly versatile closed-loop mouse BMI with an online model of a real-world prosthetic arm. We describe this setup and illustrate how it allows to explore the efficiency of different input and output coding strategies given a realistic modelling of the interactions between a commercial bidirectional prosthesis and its environment.

Rats use their whiskers to extract sensory information from their environment. While exploring, they analyze peripheral stimuli distributed over several whiskers. Previous studies have reported cross-whisker integration of information at several levels of the neuronal pathways from whisker follicles to the somatosensory cortex. In the present study, we investigated the possible coupling between whiskers at a preneuronal level, transmitted by the skin and muscles between follicles. First, we quantified the movement induced on one whisker by deflecting another whisker. Our results show significant mechanical coupling, predominantly when a given whisker’s caudal neighbor in the same row is deflected. The magnitude of the effect was correlated with the diameter of the deflected whisker. In addition to changes in whisker angle, we observed curvature changes when the whisker shaft was constrained distally from the base. Second, we found that trigeminal ganglion neurons innervating a given whisker follicle fire action potentials in response to high-magnitude deflections of an adjacent whisker. This functional coupling also shows a bias toward the caudal neighbor located in the same row. Finally, we designed a two-whisker biomechanical model to investigate transmission of forces across follicles. Analysis of the whisker-follicle contact forces suggests that activation of mechanoreceptors in the ring sinus region could account for our electrophysiological results. The model can fully explain the observed caudal bias by the gradient in whisker diameter, with possible contribution of the intrinsic muscles connecting follicles. Overall, our study demonstrates the functional relevance of mechanical coupling on early information processing in the whisker system. NEW & NOTEWORTHY Rodents explore their environment actively by touching objects with their whiskers. A major challenge is to understand how sensory inputs from different whiskers are merged together to form a coherent tactile percept. We demonstrate that external sensory events on one whisker can influence the position of another whisker and, importantly, that they can trigger the activity of mechanoreceptors at its base. This cross-whisker interaction occurs pre-neuronally, through mechanical transmission of forces in the skin.

Objective. The development of brain–machine interfaces (BMIs) brings new prospects to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a fragile object, or ensure a smooth trajectory. We have developed a BMI in the mouse that includes a rich artificial somatosensory-like cortical feedback. Approach. Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (vM1) and delivers feedback simultaneously via a low-latency, high-refresh-rate, spatially structured photo-stimulation of the whisker primary somatosensory cortex (vS1), based on a mapping obtained by intrinsic imaging. Main results. We demonstrate the operation of the loop and show that mice can detect the neuronal spiking in vS1 triggered by the photo-stimulations. Finally, we show that the mice can learn a behavioral task relying solely on the artificial inputs and outputs of the closed-loop BMI. Significance. This is the first motor BMI that includes a short-latency, intracortical, somatosensory-like feedback. It will be a useful platform to discover efficient cortical feedback schemes towards future human BMI applications.

After half a century of research, the sensory features coded by neurons of the rodent barrel cortex remain poorly understood. Still, views of the sensory representation of whisker information are increasingly shifting from a labeled line representation of single-whisker deflections to a selectivity for specific elements of the complex statistics of the multi-whisker deflection patterns that take place during spontaneous rodent behavior - so called natural tactile scenes. Here we review the current knowledge regarding the coding of patterns of whisker stimuli by barrel cortex neurons, from responses to single-whisker deflections to the representation of complex tactile scenes. A number of multi-whisker tunings have already been identified, including center-surround feature extraction, angular tuning during edge-like multi-whisker deflections, and even tuning to specific statistical properties of the tactile scene such as the level of correlation across whiskers. However, a more general model of the representation of multi-whisker information in the barrel cortex is still missing. This is in part because of the lack of a human intuition regarding the perception emerging from a whisker system, but also because in contrast to other primary sensory cortices such as the visual cortex, the spatial feature selectivity of barrel cortex neurons rests on highly nonlinear interactions that remained hidden to classical receptive field approaches.

Tactile perception in rodents depends on simultaneous, multi-whisker contacts with objects. Although it is known that neurons in secondary somatosensory cortex (wS2) respond to individual deflections of many whiskers, wS2's precise function remains unknown. The convergence of information from multiple whiskers into wS2 neurons suggests that they are good candidates for integrating multi-whisker information. Here, we apply stimulation patterns with rich dynamics simultaneously to 24 macro-vibrissae of rats while recording large populations of single neurons. Varying inter-whisker correlations without changing single whisker statistics, we observe pronounced supra-linear multi-whisker integration. Using novel analysis methods, we show that continuous multi-whisker movements contribute to the firing of wS2 neurons over long temporal windows, facilitating spatio-temporal integration. In contrast, primary cortex (wS1) neurons encode fine features of whisker movements on precise temporal scales. These results provide the first description of wS2's representation during multi-whisker stimulation and outline its specialized role in parallel to wS1 tactile processing.

Rodents explore their environment with an array of whiskers, inducing complex patterns of whisker deflections. Cortical neuronal networks can extract global properties of tactile scenes. In the primary somatosensory cortex, the information relative to the global direction of a spatiotemporal sequence of whisker deflections can be extracted at the single neuron level. To further understand how the cortical network integrates multi-whisker inputs, we imaged and recorded the mouse barrel cortex activity evoked by sequences of multi-whisker deflections generating global motions in different directions. A majority of barrel-related cortical columns show a direction preference for global motions with an overall preference for caudo-ventral directions. Responses to global motions being highly sublinear, the identity of the first deflected whiskers is highly salient but does not seem to determine the global direction preference. Our results further demonstrate that the global direction preference is spatially organized throughout the barrel cortex at a supra-columnar scale.