Inhibitory interneuron diversity is a central feature of cortical circuits. The IN-CODE consortium seeks to combine large-scale recordings of interneuron types with machine-learning tools to identify the role of their physiological features, connectivity motifs, and cooperativity in cognitive functions.

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.

Traditional approaches probe cognition by aligning brain activity to external stimuli. Building on evidence that transient oscillatory activity marks neural responses, we articulate burst-related potentials (BRPs) as temporal anchors for studying cognitive processes poorly captured by stimulus-locked analyses.

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.

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.

This paper presents the system architecture for an implant concept called NeuroBus . Tiny distributed direct digitizing neural recorder ASICs on an ultra-flexible polyimide substrate are connected in a bus-like structure, allowing short connections between electrode and recording front-end with low wiring effort and high customizability. The small size (344 μm x 294 μm) of the ASICs and the ultraflexible substrate allow a low bending stiffness, enabling the implant to adapt to the curvature of the brain and achieving high structural biocompatibility. We introduce the architecture, the integrated building blocks, and the post-CMOS processes required to realize a NeuroBus , and we characterize the prototyped direct digitizing neural recorder front-end as well as polyimide-based ECoG brain interface. A rodent animal model is further used to validate the joint capability of the recording front-end and thin-film electrode array.

How do brains—biological or artificial—respond and adapt to an ever-changing environment? In a recent meeting, experts from various fields of neuroscience and artificial intelligence met to discuss internal world models in brains and machines, arguing for an interdisciplinary approach to gain deeper insights into the underlying mechanisms.

Effective pain management in laboratory animals is crucial for both animal welfare and the reliability of scientific research. We retrospectively examined the effects of carprofen as post-operative analgesia in Sprague Dawley rats following stereotactic surgery. Our data indicate that administering carprofen twice daily (5 mg/kg), as currently recommended by Die Gesellschaft für Versuchstierkunde/Society for Laboratory Animal Science (GV-SOLAS), led to adverse effects such as reduced food and water intake, disrupted fecal excretion, and abdominal bloating consistent with peritonitis. Continued administration exacerbated these symptoms, with post-mortem findings of intestinal obstructions and ulcers. However, when the frequency was reduced to once daily, such adverse symptoms were not observed. These results are based on incidental data collected from various neuroscientific experiments, resulting in small and uneven sample groups across various experimental cohorts. The inherent imbalances in these groups present challenges for statistical interpretation. While the findings suggest that less frequent carprofen use may reduce adverse effects, the surgical interventions and concurrent use of other drugs in these experiments likely exacerbated these outcomes. Further investigation into the interactions between carprofen, surgical stress, and other perioperative factors is needed to refine analgesia protocols in laboratory animals. Despite these limitations, these observations contribute to understanding analgesia protocols and may assist in improving animal welfare practices.

Goal-directed action control and behavioral flexibility are prerequisites for effective, adaptive behavior. Both abilities rely on functional motor inhibition, which is linked to the prefrontal cortex (PFC), where distinct subsections collaborate in functional networks. How these PFC subsections interact and which roles they play during motor inhibition remains incompletely understood. In this study, we employed an action-preparation task in rats, combined with bidirectional optogenetic interventions, opto-fMRI, single unit electrophysiology and local field potential synchrony measurements across PFC subsections. Our findings support a clear and simple model of action inhibition within the prefrontal network. This model suggests prelimbic cortex (PL) as an input-dependent switch between motor inhibition and execution, modulated by an infralimbic cortex (IL)-dominated network. This distribution of tasks allows the PL to mediate goal-directed action while the IL ensures behavioral flexibility. ### Competing Interest Statement The authors have declared no competing interest.

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.

The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.

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 rich repertoire of skilled mammalian behavior is the product of neural circuits that generate robust and flexible patterns of activity distributed across populations of neurons. Decades of associative studies have linked many behaviors to specific patterns of population activity, but association alone cannot reveal the dynamical mechanisms that shape those patterns. Are local neural circuits high-dimensional dynamical reservoirs able to generate arbitrary superpositions of patterns with appropriate excitation? Or might circuit dynamics be shaped in response to behavioral context so as to generate only the low-dimensional patterns needed for the task at hand? Here, we address these questions within primate motor cortex by delivering optogenetic and electrical microstimulation perturbations during reaching behavior. We develop a novel analytic approach that relates measured activity to theoretically tractable, dynamical models of excitatory and inhibitory neurons. This computational model captures the dynamical effects of these perturbations and demonstrates that motor cortical activity during reaching is shaped by a self-contained, low-dimensional dynamical system. The subspace containing task-relevant dynamics proves to be oriented so as to be robust to strong non-normal amplification within cortical circuits. This task dynamics space exhibits a privileged causal relationship with behavior, in that stimulation in motor cortex perturb reach kinematics only to the extent that it alters neural states within this subspace. Our results resolve long-standing questions about the dynamical structure of cortical activity associated with movement, and illuminate the dynamical perturbation experiments needed to understand how neural circuits throughout the brain generate complex behavior.

Current neural decoding methods typically aim at explaining behavior based on neural activity via supervised learning. However, since generally there is a strong connection between learning of subjects and their expectations on long-term rewards, we propose NeuRL, an inverse reinforcement learning approach that (1) extracts an intrinsic reward function from collected trajectories of a subject in closed form, (2) maps neural signals to this intrinsic reward to account for long-term dependencies in the behavior and (3) predicts the simulated behavior for unseen neural signals by extracting Q-values and the corresponding Boltzmann policy based on the intrinsic reward values for these unseen neural signals. We show that NeuRL leads to better generalization and improved decoding performance compared to supervised approaches. We study the behavior of rats in a response-preparation task and evaluate the performance of NeuRL within simulated inhibition and per-trial behavior prediction. By assigning clear functional roles to defined neuronal populations our approach offers a new interpretation tool for complex neuronal data with testable predictions. In per-trial behavior prediction, our approach furthermore improves accuracy by up to 15% compared to traditional methods.

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 interaction between spontaneous and externally evoked neuronal activity is fundamental for a functional brain. Increasing evidence suggests that bursts of high-power oscillations in the 15- to 30-Hz beta-band represent activation of internally generated events and mask perception of external cues. Yet demonstration of the effect of beta-power modulation on perception in real time is missing, and little is known about the underlying mechanism. Here, we used a closed-loop stimulus-intensity adjustment system based on online burst-occupancy analyses in rats involved in a forepaw vibrotactile detection task. We found that the masking influence of burst occupancy on perception can be counterbalanced in real time by adjusting the vibration amplitude. Offline analysis of firing rates (FRs) and local field potentials across cortical layers and frequency bands confirmed that beta-power in the somatosensory cortex anticorrelated with sensory evoked responses. Mechanistically, bursts in all bands were accompanied by transient synchronization of cell assemblies, but only beta-bursts were followed by a reduction of FR. Our closed loop approach reveals that spontaneous beta-bursts reflect a dynamic state that competes with external stimuli.

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.