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.

Recent discoveries involving pyramidal neurons with two zones of input integration (apical and basal), the contextual information they provide, and the neural circuitry and neuromodulation systems in which they are embedded, can contribute to our understanding of disorganized symptoms in schizophrenia, including changes in their expression from the prodromal to chronic stages of the disorder. We demonstrate that the concepts and findings inherent to apical integration theory provide evidence in support of some of the seminal understandings of schizophrenia. In particular, considering the functions of two-point neurons allows for a novel neurobiological understanding of both their role in integrating different sources of information and the impairments in integrative functions that have long been hypothesized to be a defining characteristic of schizophrenia. We demonstrate how the weakening of integrative functions, in multiple domains including perception, cognition, language, behavior, and phenomenology, can be understood as arising from: 1) failures of apical dendrite activity to exert modulatory effects on neuronal output, and 2) reduced activity in the cortico-thalamic networks that support this form of contextual modulation. Later sections address topics such as implications of our view for understanding consciousness in schizophrenia, limitations, and future directions.

Flexible learning relies on integrating sensory and contextual information to adjust behavioral output in different environments. The anterolateral motor cortex (ALM) is a frontal area critical for action selection in rodents. We found that inputs critical to decision-making converge on the apical tuft dendrites of layer 5b pyramidal neurons in ALM. We therefore investigated the role of these dendrites in a rule-switching paradigm. Activation of dendrite-inhibiting layer 1 interneurons impaired relearning, without affecting previously learned behavior. This inhibition profoundly suppressed global calcium activity in dendritic shafts but not local transients in spines, while additionally reducing burst firing. Moreover, excitatory synaptic inputs to tuft dendrites exhibited rule-dependent clustering. We conclude that dendritic calcium signaling is a key computational component of flexible learning.

Abstract Adaptive behavior requires updating responses when contingencies change while preserving prior associations and the capacity to learn a new. How this trade-off is resolved remains unknown. Here, we combined in vivo imaging of apical tuft spines in the secondary motor cortex (M2) with biologically constrained network modeling in mice performing a cross-modal rule-switch task. M2 inactivation impaired rule-switching but not learning or maintenance, identifying it as a conflict resolution substrate. Adaptation was accompanied by elevated spine turnover concentrated within stable dendritic hotspots, in which the formation, elimination and clustering of new spines were coupled and pre-existing spines were lost early. A network model reproduces these dynamics and predicts that dendritic hotspots are critical for resource-efficient adaptation. Within these reusable domains, spines encoding the prior rule, are replaced by newly-relevant ones via sharing of plasticity-related resources. Preventing reuse increases both the plasticity and the engram size requirements to encode the two rules. We propose that dendritic hotspots provide a mechanistic substrate for efficient adaptive learning.

Abstract Representational drift, the gradual evolution of neural population codes over days, has been widely documented at the level of single neurons. However, whether drift is organized across larger cortical populations and spatial scales remains unclear. Here, we examined cortex-wide population dynamics under tightly controlled sensory input and behavior. Using widefield calcium imaging, we longitudinally recorded excitatory activity in Layers 2/3 (L2/3) or Layer 5 (L5) across 25 cortical areas while mice performed a whisker-based texture discrimination task. Sensory-evoked activity was tracked over five consecutive days during stable task performance. Population activity patterns reorganized over days, across the cortex, in both layers. This reorganization followed distinct laminar motifs: notably L5 responses exhibited a widespread, monotonic decrease in activity across most cortical areas, whereas L2/3 responses showed spatially localized and heterogeneous changes that were strongest during the sensory period. These laminar differences extended beyond the stimulus period, with L5 exhibiting more prolonged temporal engagement than L2/3. Together, these findings indicate that representational drift can unfold as a process that is coordinated across cortex with distinct laminar profiles. Thus, drift may reflect a structured feature of cortex-wide circuit dynamics.

A key function of the brain is to move the body through a rich, complex environment. When rodents engage with their environment, they move their whiskers as they extract tactile information. Even though the study of whisking has a long history, the details of individual whisker movements bilaterally, of nose movement, of stereotypy and variability in an active whisking to touch behavior are unknown. Here we trained five head-fixed male and 11 female mice in a go-cue task to move a whisker to touch a sensor. Our analysis of orofacial movements shows that mice specifically control movement of the whisker they use to touch and that as they move their whiskers, they move their nose and apply forces on the head post in a manner that reflects the behavioral epoch, i.e., whether go-cue triggered movement had begun or a whisker was touching the sensor. Importantly, mice control the setpoint, amplitude, and frequency of movement of whiskers bilaterally and individually. Additionally, even though mice achieved the goal of the task-to touch the sensor within 2 s-how they coordinated movement of the nose and forces on head post with movement of individual whiskers was stereotyped and related to the distance they needed to move a whisker to touch the sensor. Our work shows how stereotyped mouse behavior can be, and it emphasizes both the level of fine motor control mice can exert over individual whiskers and the extent of facial movements in a goal-directed whisking-to-touch task.

Binge feeding commonly leads to overeating. Experiencing flavor during food consumption contributes to satiation. Still, the interactions between flavor, binge feeding, and food intake remain unknown. Using miniscopes for in vivo calcium imaging in the anterior piriform cortex (aPC) in freely moving mice, we identified specific excitatory neuronal responses to different food flavors during slow feeding. Switching from slow feeding to binge feeding transformed these specific responses into an unspecific global suppression of neuronal activity. Consummatory aPC suppression scaled with food value. GABAergic neurons in the olfactory tubercle (OT) projected to the aPC and mirrored activity patterns in the aPC under different feeding conditions, consistent with transmitting a value signal. Closed-loop optogenetic manipulations demonstrated that suppressing the aPC during binge bouts reduces satiation by selectively prolonging feeding bouts. We propose that aPC suppression by the OT enhances food intake by reducing sensory satiation during binge feeding-associated states of high motivation.

Head fixation of rodents is a widely utilized and important technique that enables laboratories to measure brain activity during behavior, but head fixation can increase stress which affects both behavior and underlying brain activity, as well as animal welfare. It is therefore critical to keep stress levels low, yet it is particularly challenging to assess stress in immobilized, head-fixed rodents. Conventional stress evaluation methods, such as blood corticosterone analysis, are labor-intensive and conducted post hoc, and In situ approaches require experienced personnel and constant vigilance during experiments. In this study, we present MouseCare, an automated software solution for immediate stress detection by real-time facial feature video analysis in head-fixed mice. MouseCare performs on par with or better than conventional stress measures, and is significantly less labor-intensive. This approach enables objective measures of stress that are needed to determine when an experiment can commence, but also when it should be stopped. We conclude that MouseCare offers a cost-effective and easily implementable strategy to manage stress levels in rodents that can increase data quality and improve animal welfare.

The dentate gyrus of the hippocampus is targeted by axons from serotonin raphe neurons, where the neurotransmitter modulates adult neurogenesis and antidepressant action, and mediates the neurogenic effect of running. Whether running-induced cell proliferation is directly mediated by serotonin remains unknown. Here, we took advantage of Tph2-ChR2-YFP transgenic mice in which the light-sensitive protein channelrhodopsin-2 (ChR2) is specifically expressed in tryptophan hydroxylase 2 (TPH2)-expressing neurons. We selectively activated serotonin neurons via optogenetics and determined the effect on cell proliferation in the dentate gyrus. Our data reveal a significant reduction in the number of newly generated cells upon overnight raphe stimulation. The decrease in cell proliferation was absent when serotonin neurons were light-activated for six consecutive nights. However, we observed an interhemispheric difference in BrdU-positive cell numbers. We conclude that acute network dynamics occur between serotonin raphe neurons and the hippocampus, directly affecting precursor cell proliferation.

Behavioral responses to threats — such as fleeing, freezing, or fighting — can be either innate or shaped by learning and context. Here, we investigated whether mice exhibit fear of predators across four experimental contexts: one in a novel head-fixed condition and three in established, freely moving scenarios. In head-fixed mice, we measured the behavioral outcome and response to a live rat. Mice were water-deprived and habituated to walk on a treadmill that controlled a virtual environment and reward delivery. After meeting performance criteria, baseline data were collected in one session, followed by a test session in which the mice were exposed to a live rat. Despite the presence of the predator, most (5 out of 7) mice continued to forage at baseline levels; however, individual mice showed significant alterations in one or more of the following measures: running speed, pupil size, eye movement, and posture. To assess how behavioral context and physical restraint influence predator responses, we exposed 36 naive, freely moving mice to fear-inducing stimuli — including looming visual cues, rat odor, and a live rat. Even in these conditions, a substantial proportion of mice failed to exhibit classical defensive responses such as avoidance or escape. Notably, when presented with a freely moving rat, only about half of the mice displayed avoidance behavior. Together, these findings suggest that mice do not universally express innate fear behaviors such as avoidance or fleeing, even in ethologically relevant predator encounters. Instead, their responses appear to be context- dependent and variable, challenging common assumptions about automatic defensive reactions in rodents. Graphical Abstract We implemented a novel head-fixed foraging paradigm in mice to observe defensive responses to the simulated appearance of a live rat, but did not observe expected motifs (such as fleeing or freezing). To verify, we also applied three paradigms with a larger number of freely moving mice, including presenting a looming stimulus, rat odor, and finally a live rat. Unexpectedly we observed in these paradigms also no, or only limited, defensive behaviors. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, 246731133, 250048060, 267823436, 327654276 — SFB 1315 European Commission, https://ror.org/00k4n6c32, Horizon 2020 Research And Innovation Program and Euratom Research and Training Program 2014–2018 (under grant agreement No. 670118), Human Brain Project, 720270 (SGA1), Human Brain Project, 785907 (SGA2), Human Brain Project, 945539 (SGA3) Einstein Foundation, EVF-2017-363, Visiting Fellowship EVF-2019-508 Alexander von Humboldt Foundation, Wilhelm Bessel Research Award [1]: pending:yes

Adaptive behavior is critically dependent on associative learning, where environmental cues are linked with subsequent positive or negative outcomes. In mammals, primary neocortical sensory areas serve as pivotal nodes in this process, processing stimuli and distributing information to cortical and subcortical networks. Layer 5 (L5) of the cortex comprises two types of pyramidal projection neurons - intratelencephalic (IT) and extratelencephalic (ET) neurons - each with distinct downstream targets. Despite the crucial function of L5 as a main output node of the cortex, the specific contributions of these L5 neuronal subtypes to associative learning remain poorly understood. In the present study, by leveraging transgenic mouse lines, we distinguished IT and ET neurons in the primary somatosensory cortex and examined their roles in a whisker-based frequency-discrimination learning task. Longitudinal two-photon calcium imaging revealed distinct response characteristics between IT and ET neurons throughout learning. Interestingly, the activity of IT neurons hardly changed over the five days of learning, while the activity of ET neurons developed robustly. Furthermore, IT neurons appeared to show stimuli encoding from the beginning, whereas the ET neurons became increasingly responsive to stimuli associated with reward. Chemogenetic silencing of either IT or ET neurons both impaired learning, but in strikingly distinct ways, each associated with a different phase of learning. By modeling the response characteristics of IT and ET neurons using a reinforcement learning framework, we show that IT neurons primarily encode sensory stimuli, and their representations are critical for forming stimulus-reward associations. ET neurons instead represent the value of the stimulus, used for refining behavior. Thus, our results delineate the distinct roles of L5 IT and ET neurons, underscoring their integral and complementary contributions to associative learning. ### Competing Interest Statement The authors have declared no competing interest.

Contemporary models of perceptual awareness lack tractable neurobiological constraints. Inspired by recent cellular recordings in a mouse model of tactile threshold detection, we constructed a biophysical model of perceptual awareness that incorporated essential features of thalamocortical anatomy and cellular physiology. Our model reproduced, and mechanistically explains, the key in vivo neural and behavioural signatures of perceptual awareness in the mouse model, as well as the response to a set of causal perturbations. We generalised the same model (with identical parameters) to a more complex task - visual rivalry - and found that the same thalamic-mediated mechanism of perceptual awareness determined perceptual dominance. This led to the generation of a set of novel, and directly testable, electrophysiological predictions. Analyses of the model based on dynamical systems theory show that perceptual awareness in simulations of both threshold detection and visual rivalry arises from the emergent systems-level dynamics of thalamocortical loops.

Artificial neural networks are becoming more advanced and human-like in detail and behavior. The notion that machines mimicking human brain computations might be conscious has recently caused growing unease. Here, we explored a common computational functionalist view, which holds that consciousness emerges when the right computations occur—whether in a machine or a biological brain. To test this view, we simulated a simple computation in an artificial subject’s “brain” and recorded each neuron’s activity when the subject was presented with a visual stimulus. We then replayed these recorded signals back into the same neurons, degrading the computation by effectively eliminating all alternative activity patterns that otherwise might have occurred (i.e., the counterfactuals). We identified a special case in which the replay did nothing to the subject’s ongoing brain activity—allowing it to evolve naturally in response to a stimulus—but still degraded the computation by erasing the counterfactuals. This paradoxical outcome points to a disconnect between ongoing neural activity and the underlying computational structure, which challenges the notion that consciousness arises from computation in artificial or biological brains.

Learning to link sensory information to motor actions involves dynamic coordination across cortical layers and regions. However, the involvement of a particular layer in learning, especially from a cortex-wide perspective, is relatively unknown. Using wide-field calcium imaging in mice as they learn a whisker-based go/no-go task, we tracked activity in layers 2/3 or 5 (L2/3 or L5) across 25 cortical areas. A surprising initial effect of learning was that activity in L5 but not in L2/3 was globally suppressed at auditory cue onset. As the texture comes into touch, we found that L2/3 displayed learning-related enhancements in higher order association areas rostrolateral (RL) and secondary somatosensory (S2), whereas L5 in these areas oppositely decreased. During texture touch, the barrel cortex (BC) displayed similar learning-related enhancement in both layers. As sensory information is transformed into a motor action, there was a frontal/posterior divergence that emerges after learning, in which L5 was enhanced in the frontal cortex and L2/3 was suppressed in the posterior cortex. In general, learning related correlations were often stronger between distant cortical layers than within the same column, suggesting that learning drives laminar interactions that transcend traditional columnar organization. Together, these results reveal that learning orchestrates a dynamic interplay of activity across space, time and cortical layers. Our findings emphasize the critical role of laminar architecture in shaping cortical plasticity and support the view that layer-specific circuits are fundamental to sensorimotor learning. ### Competing Interest Statement The authors have declared no competing interest. Einstein Foundation, https://ror.org/03s0fv852, A-2021-644 European Research Council, https://ror.org/0472cxd90, 101040378

Neurons in primary visual cortex are driven by feedforward visual inputs and top-down contextual inputs. The nature of this contextual information is difficult to study, as responses to feedforward and top-down inputs overlap in time and are difficult to disentangle experimentally. To address this issue, we measured responses to natural images and partially occluded versions of these images in the visual cortex of mice. Assessing neuronal responses before and after familiarizing mice with the non-occluded images allowed us to study experience-dependent and stimulus-specific contextual responses in pyramidal cells (PyCs) in cortical layers 2/3 and 5 in the absence of feedforward input. Surprisingly, in the same retinotopic region of cortex, we found that separate populations of PyCs in layer 2/3 responded to occluded and non-occluded images. Responses of PyCs selective for occluded images were strengthened upon familiarization and decoding analysis revealed they contained image-specific information, suggesting that they signaled the absence of predicted visual stimuli. Responses of PyCs selective for non-occluded scenes were weaker for familiarized images but stronger for unfamiliar images, suggesting that these neurons signaled the presence of unpredicted visual stimuli. Layer 5 also contained PyCs preferring either feedforward or contextual inputs, but their responses were more complex and strengthening of responses to occluded images required task engagement. The results show that visual experience decreases the activity of neurons responding to known feedforward inputs but increases the activity of neurons responding to contextual inputs tied to expected stimuli. ### Competing Interest Statement The authors have declared no competing interest.

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

High-speed volumetric imaging is crucial for observing fast and distributed processes such as neuronal activity. Multiphoton microscopy helps to mitigate scattering effects inside tissue, but the standard raster scanning approach limits achievable volume rates. Random access point scanning can lead to a considerable speed-up by sampling only pre-selected locations, but existing techniques based on acousto-optic deflectors are still limited to a point rate of up to similar to 50 kHz. This limits the number of parallel targets at the high acquisition rates necessary, for example, in voltage imaging or imaging of fast synaptic events. Here, we introduce SPARCLS, a method for 3D random access point scanning at up to 340 kHz using a single 1D phase modulator. We show the potential of this method by imaging synaptic events with fluorescent glutamate sensors in mammalian organotypic slices as well as in zebrafish larvae. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Hippocampal oscillations span from slow to high-frequency bands that are linked to different memory stages and behavioral states. We show that fast spiking basket cells (FSBCs) with bimodal nonlinear dendritic trees modulate these oscillations. Supralinear FSBC dendritic activation enhances high-frequency oscillations, while sublinear activation increases slow oscillatory power, adjusting the Excitation/Inhibition balance in the network. This underscores a new link between FSBCs nonlinear dendritic integration and memory-related oscillations. ### Competing Interest Statement The authors have declared no competing interest.