Preparing and initiating movements at the right time is critical for goal-directed behavior. Before movement execution, motor cortical areas exhibit preparatory activity, which decreases from premotor to primary motor areas. During the shift from preparation to execution activity in neural state space transitions from movement-null to movement-potent dimensions. However, the circuit-level mechanisms underlying this shift remain unresolved. Here, we demonstrate that projections from the rat premotor cortex (rostral forelimb area [RFA]) to the primary motor cortex (caudal forelimb area [CFA]) encode primarily pre-movement activity. Optogenetic inhibition of these projections has behavioral effects comparable to inhibiting either RFA or CFA alone. During preparation, RFA projections enhance and suppress CFA neurons similarly, affecting activity along CFA's preparatory dimension. During movement, RFA's influence shifts predominantly to excitatory, aligning with CFA's movement-potent dimension. These results establish a mechanistic link between neural state space concepts and underlying circuit mechanisms, providing an intuitive model for movement control.

To facilitate optogenetic experiments in neuroscience, we designed a cost-effective (<40 €) and versatile laser control system, FreiLaser, based on the Raspberry Pi Pico microcontroller running CircuitPython. FreiLaser allows precise control over various stimulation parameters for up to four lasers, utilizing both analog and digital signals. It features a user-friendly graphical interface for parameter configuration and real-time visualization, as well as an API for seamless integration with existing experimental setups. The system also includes a built-in mask controller to prevent behavioral bias by synchronizing masking LEDs with laser pulses. Validation tests confirmed that FreiLaser generates stable, temporally precise control signals, suitable for a range of stimulation patterns. The system’s flexibility, ease of use, and low cost make it an invaluable tool for researchers conducting optogenetic and behavioral studies. Our open-source design ensures accessibility and adaptability for a wide range of experimental needs.

In the pursuit of comprehending decision-making, behavioral neuroscience has made significant progress, aided by mathematical models in recent years. Among various approaches, Inverse Reinforcement Learning (IRL) stands out as a promising technique, distinguishing itself from other paradigms through its ability to circumvent the necessity for a reward function in characterizing observed behavior. Nevertheless, the widespread adoption of IRL within the field of neuroscience remains limited. This constraint may be attributed, in part, to the prevailing assumption in many existing IRL frameworks that animals exhibit a singular intention throughout a given task, wherein their behavior is optimized based on a single static reward function. In an effort to overcome this limitation, we propose the class of Latent (Markov) Variable Inverse Q-learning (L(M)V-IQL) algorithms, a novel IRL framework designed to accommodate multiple discrete intrinsic rewards. We formulate an Expectation-Maximization approach to cluster observed trajectories into multiple intentions, and subsequently solve the IRL problem independently for each intention. We illustrate the application of L(M)V-IQL through simulated experiments, followed by its utilization on a dataset of mice engaged in a two-armed bandit task. Our methods exhibit exceptional proficiency in discerning animal intentions and yield interpretable reward functions corresponding to each identified intention. We anticipate that this progress will open up new possibilities in neuroscience and psychology, serving as an important advancement in elucidating the intricacies of animal decision-making and uncovering underlying brain mechanisms.

Osteocytes sense and respond to mechanical force by controlling the activity of other bone cells. However, the mechanisms by which osteocytes sense mechanical input and transmit biological signals remain unclear. Voltage-sensitive calcium channels (VSCCs) regulate calcium (Ca2+) influx in response to external stimuli. Inhibition or deletion of VSCCs impairs osteogenesis and skeletal responses to mechanical loading. VSCC activity is influenced by its auxiliary subunits, which bind the channel's α1 pore-forming subunit to alter intracellular Ca2+ concentrations. The α2δ1 auxiliary subunit associates with the pore-forming subunit via a glycosylphosphatidylinositol anchor and regulates the channel's calcium-gating kinetics. Knockdown of α2δ1 in osteocytes impairs responses to membrane stretch, and global deletion of α2δ1 in mice results in osteopenia and impaired skeletal responses to loading in vivo. Therefore, we hypothesized that the α2δ1 subunit functions as a mechanotransducer, and its deletion in osteocytes would impair skeletal development and load-induced bone formation. Mice (C57BL/6) with LoxP sequences flanking Cacna2d1, the gene encoding α2δ1, were crossed with mice expressing Cre under the control of the Dmp1 promoter (10 kb). Deletion of α2δ1 in osteocytes and late-stage osteoblasts decreased femoral bone quantity (P < .05) by DXA, reduced relative osteoid surface (P < .05), and altered osteoblast and osteocyte regulatory gene expression (P < .01). Cacna2d1f/f, Cre + male mice displayed decreased femoral strength and lower 10-wk cancellous bone in vivo micro-computed tomography measurements at the proximal tibia (P < .01) compared to controls, whereas Cacna2d1f/f, Cre + female mice showed impaired 20-wk cancellous and cortical bone ex vivo micro-computed tomography measurements (P < .05) vs controls. Deletion of α2δ1 in osteocytes and late-stage osteoblasts suppressed load-induced calcium signaling in vivo and decreased anabolic responses to mechanical loading in male mice, demonstrating decreased mechanosensitivity. Collectively, the α2δ1 auxiliary subunit is essential for the regulation of osteoid-formation, femur strength, and load-induced bone formation in male mice.

In advancing the understanding of natural decision-making processes, inverse reinforcement learning (IRL) methods have proven instrumental in reconstructing animal's intentions underlying complex behaviors. Given the recent development of a continuous-time multi-intention IRL framework, there has been persistent inquiry into inferring discrete time-varying rewards with IRL. To address this challenge, we introduce the class of hierarchical inverse Q-learning (HIQL) algorithms. Through an unsupervised learning process, HIQL divides expert trajectories into multiple intention segments, and solves the IRL problem independently for each. Applying HIQL to simulated experiments and several real animal behavior datasets, our approach outperforms current benchmarks in behavior prediction and produces interpretable reward functions. Our results suggest that the intention transition dynamics underlying complex decision-making behavior is better modeled by a step function instead of a smoothly varying function. This advancement holds promise for neuroscience and cognitive science, contributing to a deeper understanding of decision-making and uncovering underlying brain mechanisms.

Deciphering the neural code underlying goal-directed behavior is a long-term mission in neuroscience 1,2 . Neurons exhibiting preparation and movement-related activity are intermingled in the premotor and motor cortices 3,4 , thus concealing the neural code of planned movements. We employed a combination of electrophysiology, pathway-specific optogenetics, phototagging, and inverse reinforcement learning (RL) to elucidate the role of defined neuronal subpopulations in the rat rostral and caudal forelimb areas (RFA and CFA), which correspond to the premotor and motor cortical areas. The inverse RL enabled the functional dissection of spatially intermingled neuronal subpopulations, complementing our pathway-specific optogenetic manipulations and unveiling differential functions of the preparation and movement subpopulations projecting from RFA to CFA. Our results show that the projecting preparation subpopulation suppresses movements, whereas the projecting movement subpopulation promotes actions. We found the influence of RFA on CFA to be adaptable, with the projection either inhibiting or exciting neurons in the superficial and deep CFA layers, depending on context and task phase. These complex interactions between RFA and CFA likely involve the differential recruitment of inhibitory interneurons in the CFA, which is supported by our electron microscopy analysis of the connectivity between these regions. We provide here unprecedented mechanistic insights into how the premotor and primary motor cortices are functionally and structurally interlinked with the potential to advance neuroprosthetics. Graphical abstract This study provides mechanistic insights into the interactions between the rostral forelimb area (RFA) and the caudal forelimb area (CFA). Specifically, we provide evidence for a differential impact of RFA on CFA depending on the task phase and the targeted CFA layers. RFA contains at least two spatially intermingled subpopulations - one related to movement preparation and one to movement execution. Both subpopulations project to CFA. Here we investigated the impact of these two subpopulations on the activity of the local CFA circuit as well as on the behavior in different contexts. When rats were not involved in a task, the effect of RFA was mainly excitatory in the deep CFA layers, while the superficial layers remained unaffected. This can be interpreted as a non-selective activation of the deep CFA neurons enabling a variety of spontaneous movements. During the preparation phase before a movement, the RFA had an opposite impact on the superficial and deep layers: while the superficial CFA layers were excited by RFA input, the deeper layers were mostly inhibited, minimizing movements and enabling continued holding of a lever. During the movement phase, the inhibitory effect on neurons in the deep CFA layers was counterbalanced by excitation, thus enabling a selected conduction of movements. The opposing effects during preparation and movement phase on CFA deep layers were correlated with increased firing rates of the RFA preparation and movement subpopulations, respectively, making it likely that the inhibition resulted from increased activities of these subpopulation specifically. With an electron microcopy approach we demonstrate that inhibitory and excitatory CFA neurons are directly targeted by RFA, thus providing a mechanism for the bidirectional control of CFA activity. Please note that the depicted impact of RFA on excitatory or inhibitory CFA neurons refers to net effects in this figure, not to the targeting of individual neurons.

To survive in a complex and changing environment, animals must adapt their behavior. This ability is called behavioral flexibility and is classically evaluated by a reversal learning paradigm. During such a paradigm, the animals adapt their behavior according to a change of the reward contingencies. To study these complex cog-nitive functions (from outcome evaluation to motor adaptation), we developed a versatile, low-cost, open -source platform, allowing us to investigate the neuronal correlates of behavioral flexibility with 1-photon cal-cium imaging. This platform consists of FreiBox, a novel low-cost Arduino behavioral setup, as well as further open-source tools, which we developed and integrated into our framework. FreiBox is controlled by a custom Python interface and integrates a new licking sensor (strain gauge lickometer) for controlling spatial licking be-havioral tasks. In addition to allowing both discriminative and serial reversal learning, the Arduino can track mouse licking behavior in real time to control task events in a submillisecond timescale. To complete our setup, we also developed and validated an affordable commutator, which is crucial for recording calcium imaging with the Miniscope V4 in freely moving mice. Further, we demonstrated that FreiBox can be associ-ated with 1-photon imaging and other open-source initiatives (e.g., Open Ephys) to form a versatile platform for exploring the neuronal substrates of licking-based behavioral flexibility in mice. The combination of the FreiBox behavioral setup and our low-cost commutator represents a highly competitive and complementary addition to the recently emerging battery of open-source initiatives.

In advancing the understanding of decision-making processes, Inverse Reinforcement Learning (IRL) have proven instrumental in reconstructing animal's multiple intentions amidst complex behaviors. Given the recent development of a continuous-time multi-intention IRL framework, there has been persistent inquiry into inferring discrete time-varying rewards with IRL. To tackle the challenge, we introduce Latent (Markov) Variable Inverse Q-learning (L(M)V-IQL), a novel class of IRL algorthms tailored for accommodating discrete intrinsic reward functions. Leveraging an Expectation-Maximization approach, we cluster observed expert trajectories into distinct intentions and independently solve the IRL problem for each. Demonstrating the efficacy of L(M)V-IQL through simulated experiments and its application to different real mouse behavior datasets, our approach surpasses current benchmarks in animal behavior prediction, producing interpretable reward functions. This advancement holds promise for neuroscience and cognitive science, contributing to a deeper understanding of decision-making and uncovering underlying brain mechanisms.

The impact of spontaneous movements on neuronal activity has created the need to quantify behavior. We present a versatile framework to directly capture the 3D motion of freely definable body points in a marker-free manner with high precision and reliability. Combining the tracking with neural recordings revealed multiplexing of information in the motor cortex neurons of freely moving rats. By integrating multiple behavioral variables into a model of the neural response, we derived a virtual head fixation for which the influence of specific body movements was removed. This strategy enabled us to analyze the behavior of interest (e.g., front paw movements). Thus, we unveiled an unexpectedly large fraction of neurons in the motor cortex with tuning to the paw movements, which was previously masked by body posture tuning. Once established, our framework can be efficiently applied to large datasets while minimizing the experimental workload caused by animal training and manual labeling.

Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements that are strictly controlled in experimental settings. It remains unclear how results can be carried over to less constrained behavior like that of freely moving subjects. Toward this goal, we developed a self-paced behavioral paradigm that encouraged rats to engage in different movement types. We employed bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking. These techniques revealed behavioral coupling of neurons with lateralization and an anterior–posterior gradient from the premotor to the primary sensory cortex. The structure of population activity patterns was conserved across animals despite the severe under-sampling of the total number of neurons and variations in electrode positions across individuals. We demonstrated cross-subject and cross-session generalization in a decoding task through alignments of low-dimensional neural manifolds, providing evidence of a conserved neuronal code One-sentence summary Similarities in neural population structures across the sensorimotor cortex enable generalization across animals in the decoding of unconstrained behavior. Graphical abstract Conserved structures of neural activity in freely moving rats allow for cross-subject decoding. (a) We conducted electrophysiological recordings across the bilateral sensorimotor cortex of six freely moving rats. Neural activities were projected into a low-dimensional space with LEMs ( 22 ). (b) In a decoding task, points in the aligned low-dimensional neural state space were used as input for a classifier that predicted behavioral labels. Importantly, training and testing data originated from different rats. (c) Our procedure led to successful cross-subject generalization for sessions with sufficient numbers of recorded units. The rat and brain drawings are adapted from scalablebrainatlas.incf.org and SciDraw.

The smooth conduct of movements requires simultaneous motor planning and execution according to internal goals. So far it remains unknown how such movement plans are modified without interfering with ongoing movements. Previous studies have isolated planning and execution-related neuronal activity by separating behavioral planning and movement periods in time by sensory cues. Here, we separate continuous self-paced motor planning from motor execution statistically, by experimentally minimizing the repetitiveness of the movements. This approach shows that, in the rat sensorimotor cortex, neuronal motor planning processes evolve with slower dynamics than movement-related responses. Fast-evolving neuronal activity precees skilled forelimb movements and is nested within slower dynamics. We capture this effect via high-pass filtering and confirm the results with optogenetic stimulations. The various dynamics combined with adaptation-based high-pass filtering provide a simple principle for separating concurrent motor planning and execution.

Objective. Optogenetics involves delivery of light-sensitive opsins to the target brain region, as well as introduction of optical and electrical devices to manipulate and record neural activity, respectively, from the targeted neural population. Combining these functionalities in a single implantable device is of great importance for a precise investigation of neural networks while minimizing tissue damage.Approach. We report on the development, characterization, andin vivovalidation of a multifunctional optrode that combines a silicon-based neural probe with an integrated microfluidic channel, and an optical glass fiber in a compact assembly. The silicon probe comprises an 11-µm-wide fluidic channel and 32 recording electrodes (diameter 30µm) on a tapered probe shank with a length, thickness, and maximum width of 7.5 mm, 50µm, and 150µm, respectively. The size and position of fluidic channels, electrodes, and optical fiber can be precisely tuned according to thein vivoapplication.Main results.With a total system weight of 0.97 g, our multifunctional optrode is suitable for chronicin vivoexperiments requiring simultaneous drug delivery, optical stimulation, and neural recording. We demonstrate the utility of our device in optogenetics by injecting a viral vector carrying a ChR2-construct in the prefrontal cortex and subsequent photostimulation of the transduced neurons while recording neural activity from both the target and adjacent regions in a freely moving rat for up to 9 weeks post-implantation. Additionally, we demonstrate a pharmacological application of our device by injecting GABA antagonist bicuculline in an anesthetized rat brain and simultaneously recording the electrophysiological response.Significance. Our triple-modality device enables a single-step optogenetic surgery. In comparison to conventional multi-step surgeries, our approach achieves higher spatial specificity while minimizing tissue damage.

Tactile sensation is one of our primary means to collect information about the nearby environment and thus crucial for daily activities and survival. Therefore, it is of high importance to restore sensory feedback after sensory loss. Optogenetic manipulation allows local or pathway-specific write-in of information. However, it remains elusive whether optogenetic stimulation can be interpreted as tactile sensation to guide operant behavior and how it is integrated with tactile stimuli. To address these questions, we employed a vibrotactile detection task combined with optogenetic neuromodulation in freely moving rats. By bidirectionally manipulating the activity of neurons in primary somatosensory cortex (S1), we demonstrated that optical activation as well as inhibition of S1 reduced the detection rate for vibrotactile stimuli. Interestingly, activation of corticostriatal terminals improved the detection of tactile stimuli, while inhibition of corticostriatal terminals did not affect the performance. To manipulate the corticostriatal pathway more specifically, we employed a dual viral system. Activation of corticostriatal cell bodies disturbed the tactile perception while activation of corticostriatal terminals slightly facilitated the detection of vibrotactile stimuli. In the absence of tactile stimuli, both corticostriatal cell bodies as well as terminals caused a reaction. Taken together, our data confirmed the possibility to restore sensation using optogenetics and demonstrated that S1 and its descending projections to striatum play differential roles in the neural processing underlying vibrotactile detection.

Simultaneous large-scale recordings and optogenetic interventions may hold the key to deciphering the fast-paced and multifaceted dialogue between neurons that sustains brain function. Here we have taken advantage of thin, cell-sized, optical fibers for minimally invasive optogenetics and flexible implantations. We describe a simple procedure for making those fibers side-emitting with a Lambertian emission distribution. Here we combined those fibers with silicon probes to achieve high-quality recordings and ultrafast multichannel optogenetic inhibition. Furthermore, we developed a multi-channel optical commutator and general-purpose patch-cord for flexible experiments. We demonstrate that our framework allows to conduct simultaneous laminar recordings and multifiber stimulations, 3D optogenetic stimulation, connectivity inference, and behavioral quantification in freely moving animals. Our framework paves the way for large-scale photo tagging and controlled interrogation of rapid neuronal communication in any combination of brain areas.

The smooth conduction of movements requires simultaneous motor planning and execution according to internal goals. So far it is not known how such movement plans can be modified without being distorted by ongoing movements. Previous studies have isolated planning and execution related neuronal activity by separating behavioral planning and movement periods in time by sensory cues1–7. Here, we introduced two novel tasks in which motor planning developed intrinsically. We separated this continuous self-paced motor planning statistically from motor execution by experimentally minimizing the repetitiveness of the movements. Thereby, we found that in the rat sensorimotor cortex, neuronal motor planning processes evolved with slower dynamics than movement related responses both on a sorted unit and population level. The fast evolving neuronal activity preceded skilled forelimb movements while it coincided with movements in a locomotor task. We captured this fast evolving movement related activity via a high-pass filter approach and confirmed the results with optogenetic stimulations. As biological mechanism underlying such a high pass filtering we suggest neuronal adaption. The differences in dynamics combined with a high pass filtering mechanism represents a simple principle for concurrent motor planning and execution in which planning will result in relatively slow dynamics that will not produce movements.

BACKGROUND:In accordance with the three R principles of research, animal usage should be limited as much as possible. Especially for the training of entry-level scientists in surgical techniques underlying opto- and electrophysiology, alternative training tools are required before moving on to live animals. We have developed a cost-effective rat brain model for training a wide range of surgical techniques, including, but not limited to optogenetics, electrophysiology, and intracranial pharmacological treatments.RESULTS:Our brain model creates a realistic training experience in animal surgery. The success of the surgeries (e.g. implantation accuracy) is readily assessable in cross sections of the model brain. Moreover, the model allows practicing electrophysiological recordings as well as testing for movement or light related artefacts.COMPARISON WITH EXISTING METHOD(S):The surgery and recording experience in our model closely resembles that in an actual rat in terms of the necessary techniques, considerations and time span. A few differences to an actual rat brain slightly reduce the difficulty in our model compared to a live animal. Thus, entry level scientists can first learn basic techniques in our model before moving on to the slightly more complex procedures in live animals.CONCLUSIONS:Our brain model is a useful training tool to equip scientist who are new in the field of electrophysiology and optogenetic manipulations with a basic skill set before applying it in live animals. It can be adapted to fit the desired training content or even to serve in testing and optimizing new lab equipment for more senior scientists.

Neural oscillations as important information carrier in the brain, are increasingly interpreted as transient bursts rather than as sustained oscillations. Short (<150 ms) bursts of beta-waves (15-30 Hz) have been documented in humans, monkeys and mice. These events were correlated with memory, movement and perception, and were even suggested as the primary ingredient of all beta-band activity. However, a method to measure these short-lived events in real-time and to investigate their impact on behaviour is missing. Here we present a real-time data analysis system, capable to detect short narrowband bursts, and demonstrate its usefulness to increase the beta-band burst-rate in rats. This neurofeedback training induced changes in overall oscillatory power, and bursts could be decoded from the movement of the rats, thus enabling future investigation of the role of oscillatory bursts. Karvat et al. describe a real-time digital signal-processing method to detect neural oscillations in the form of short and narrowband bursts. Using this real-time method, they show that neurofeedback increases burst-related behaviours and that these bursts can be decoded from the movements of the rats.

The increasing awareness of the impact of spontaneous movements on neuronal activity has raised the need to track behavior. We present FreiPose, a versatile learning-based framework to directly capture 3D motion of freely definable points with high precision (median error < 3.5% body length, 41.9% improvement compared to state-of-the-art) and high reliability (82.8% keypoints within < 5% body length error boundary, 72.0% improvement). The versatility of FreiPose is demonstrated in two experiments: (1) By tracking freely moving rats with simultaneous electrophysiological recordings in motor cortex, we identified neuronal tuning to behavioral states and individual paw trajectories. (2) We inferred time points of optogenetic stimulation in rat motor cortex from the measured pose across individuals and attributed the stimulation effect automatically to body parts. The versatility and accuracy of FreiPose open up new possibilities for quantifying behavior of freely moving animals and may lead to new ways of setting up experiments.

Neural oscillations are increasingly interpreted as transient bursts, yet a method to measure these short-lived events in real-time is missing. Here we present a real-time data analysis system, capable to detect short and narrowband bursts, and demonstrate its usefulness for volitional increase of beta-band burst-rate in rats. This neurofeedback-training induced changes in overall oscillatory power, and bursts could be decoded from the movement of the rats, thus enabling future investigation of the role of oscillatory bursts.