High-resolution extracellular electrophysiology is the gold standard for recording spikes from distributed neural populations, and is especially powerful when combined with optogenetics for manipulation of specific cell types with high temporal resolution. We integrated these approaches into prototype Neuropixels Opto probes, which combine electronic and photonic circuits. These devices pack 960 electrical recording sites and two sets of 14 light emitters onto a 1 cm shank, allowing spatially addressable optogenetic stimulation with blue and red light. In mouse cortex, Neuropixels Opto probes delivered high-quality recordings together with spatially addressable optogenetics, differentially activating or silencing neurons at distinct cortical depths. In mouse striatum and other deep structures, Neuropixels Opto probes delivered efficient optotagging, facilitating the identification of two cell types in parallel. Neuropixels Opto probes represent an unprecedented tool for recording, identifying, and manipulating neuronal populations.

The cerebellum governs movement and cognition through multiple lobules that carry distinct signals and connect to distinct long-range brain networks. How these specialized computations are reconciled with the brain-wide convergence required for coherent behaviour is unknown, in part because the input-output connectivity of functionally distinct lobules has not been mapped. We combined anterograde and retrograde transsynaptic viral tracing with whole-brain two-photon tomography to map the monosynaptic and disynaptic inputs and outputs of Lobule V (LV) and Simplex (LS), two lobules preferentially involved in movement and reward, respectively. Monosynaptic connectivity was strongly segregated: LV and LS received climbing fiber and mossy fiber inputs from largely non-overlapping source regions and projected to distinct targets within the cerebellar nuclei. In contrast, disynaptic inputs to LV- and LS-projecting olivary neurons arose from a shared set of forebrain, midbrain, and hindbrain regions, with the segregation re-emerging at the level of fine-scale spatial topography within these regions. Disynaptic outputs through the cerebellar nuclei likewise overlapped more than the monosynaptic projections to the nuclei themselves. These findings reveal that overlapping brain-wide pathways interface with locally specialized monosynaptic cerebellar pathways. This organizational logic could support distinct movement- and reward-related computations in the cerebellum while distributing outputs to shared downstream networks, providing an architectural substrate for both segregating and integrating cerebellar motor and cognitive functions.

Objective.Determining the intricate structure and function of neural circuits requires the ability to precisely manipulate circuit activity. Two-photon holographic optogenetics has emerged as a powerful tool for achieving this via flexible excitation of user-defined neural ensembles. However, the precision of two-photon optogenetics has been constrained by off-target stimulation (OTS), an effect where proximal non-target neurons can be unintentionally activated due to imperfect spatial confinement of light onto target neurons. New approaches are therefore needed to resolve the OTS problem.Approach.Here, we introduce a real-time computational method for mitigating OTS that first empirically samples each neuron's sensitivity to stimulation at proximal locations, and then optimizes stimulation sites using a fast, interpretable model based on adaptive non-negative basis function regression (NBFR).Main results.NBFR is highly scalable, completing model fitting for hundreds of neurons in just a few seconds and then optimizing stimulation sites in several hundred milliseconds per stimulus-fast enough for most closed-loop behavioral experiments. We characterize the performance of our approach in both simulations andin vivoexperiments in mouse hippocampus, showing its efficacy under realistic experimental conditions.Significance.Our results thus establish NBFR-based photostimulus optimization as an important addition to an emerging computational toolkit for precise yet scalable holographic optogenetics.

Learning involving the cerebellum is primarily driven by climbing fiber inputs to Purkinje cells. While the encoding properties of these instructive signals have been characterized extensively in trained animals, it is unclear how they are shaped by learning across both slow and fast timescales. Here, we use longitudinal 2-photon calcium imaging to record the dynamics of climbing fiber inputs across multiple cerebellar lobules as mice learn to execute a visuomotor integration task - slow learning - and must then rapidly adapt to a new sensorimotor coupling - fast learning. Instructive signals are spatially segregated in trained mice: Lobule V preferentially encodes movement predictively, while Lobule simplex preferentially receives reward-related feedback. Moreover, the encoding of sensorimotor and reward-related instructive signals is dynamic on both slow and fast timescales: movement-related activity emerges over the course of training, while reward-related activity progressively diminishes as mice become experts but re-emerges specifically in Lobule simplex when rewards become scarce. Finally, while the same climbing fiber inputs can change on both timescales, the magnitude and spatial organization of slow changes are not related to fast changes. Thus, climbing fiber inputs convey common yet complementary instructive signals that are dynamically shaped by learning on multiple time scales through distinct processes. ### Competing Interest Statement The authors have declared no competing interest. Wellcome Trust, 201225/Z/16/Z, 224668/Z/21/Z, 225951/Z/22/Z European Research Council, https://ror.org/0472cxd90, AdG 695709 European Molecular Biology Organization, https://ror.org/04wfr2810, ALTF 914-2015

We developed a strategy for implementing a dream experiment in systems neuroscience, where circuit manipulation is guided by the real-time readout of neural activity in behaving mice. The system integrates a state-of-the-art calcium imaging analysis package that achieves rapid online activity readout from two-photon calcium imaging, a custom hologram generation program that targets two-photon optogenetic stimulation of specific neuronal ensembles, and software modules that automate essential steps in running complex all-optical experiments. Proof-of-principle experiments demonstrate that neurons can be automatically detected and recruited into a photostimulation ensemble, closed-loop photoinhibition can be implemented immediately after fast mapping of the functional properties of cortical neurons, and targeted activation can be guided by readout of ongoing activity patterns in behaviorally relevant neuronal ensembles during decision-making.

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with previous expectations to initiate movements and make decisions(1). It is difficult to meet this challenge if different laboratories apply different analyses to different recordings in different regions during different behaviours. Here we report a comprehensive set of recordings from 621,733 neurons recorded with 699 Neuropixels probes across 139 mice in 12 laboratories. The data were obtained from mice performing a decision-making task with sensory, motor and cognitive components. The probes covered 279 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map and assess how neural activity encodes key task variables. Representations of visual stimuli transiently appeared in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of midbrain and hindbrain regions that also encoded choices. Neural responses correlated with impending motor action almost everywhere in the brain. Responses to reward delivery and consumption were also widespread. This publicly available dataset represents a resource for understanding how computations distributed across and within brain areas drive behaviour.

The neural representations of prior information about the state of the world are poorly understood1. Here, to investigate them, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with a prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions that, notably, span all levels of processing, from early sensory areas (the lateral geniculate nucleus and primary visual cortex) to motor regions (secondary and primary motor cortex and gigantocellular reticular nucleus) and high-level cortical regions (the dorsal anterior cingulate area and ventrolateral orbitofrontal cortex). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision-making areas. This study offers a brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large-scale recordings on a single standardized task.

Measuring synaptic efficacy and defining the rules for induction of synaptic plasticity at identified connections in the mammalian brain is essential for understanding how synapses contribute to learning and memory. This requires new approaches to selectively evoke presynaptic activity and measure postsynaptic responses with high spatiotemporal resolution and high sensitivity over long periods in vivo. Here we develop an all-optical approach to probe synaptic plasticity at identified cerebellar synapses in awake, behaving mice. We developed and applied JEDI-2Psub, a genetically encoded voltage indicator with increased sensitivity around resting membrane potentials, to record subthreshold and suprathreshold activity in Purkinje cell (PC) dendrites while selectively activating their granule cell (GrC) inputs using optogenetics and their climbing fiber (CF) inputs using sensory stimulation. We measured synaptic potentials and complex spike signals across the dendrites of multiple neighboring PCs, enabling us to examine correlations in voltage signals within and between neurons. We show how pairing GrC activity with sensory-evoked CF inputs can trigger long-term plasticity of inhibitory responses in PCs. These results provide a blueprint for defining the rules for plasticity induction at identified synapses in awake animals during behavior.

Determining the intricate structure and function of neural circuits requires the ability to precisely manipulate circuit activity. Two-photon holographic optogenetics has emerged as a powerful tool for achieving this via flexible excitation of user-defined neural ensembles. However, the precision of two-photon optogenetics has been constrained by off-target stimulation, an effect where proximal non-target neurons can be unintentionally activated due to imperfect spatial confinement of light onto target neurons. Here, we introduce a real-time computational approach to mitigating off-target stimulation by first empirically sampling each neuron's sensitivity to stimulation at proximal locations, and then optimizing stimulation sites using a fast, interpretable model based on adaptive non-negative basis function regression (NBFR). NBFR is highly scalable, completing model fitting for hundreds of neurons in just a few seconds and then optimizing stimulation sites in several hundred milliseconds per stimulus - fast enough for most closed-loop behavioral experiments. We characterize the performance of our approach in both simulations and in vivo experiments in mouse hippocampus, showing its efficacy under realistic experimental conditions. Our results thus establish NBFR-based photostimulus optimization as an important addition to an emerging computational toolkit for scalable precision optogenetics.

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single-neuron and single-spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). Using NP Ultra, neuronal yield in mouse visual cortex recordings increased by more than 2-fold. With ultra-high spatial resolution, we discovered that a feature of extracellular waveforms, the spatial extent or “footprint,” distinguished axonal from somatic recordings. In addition, three genetically identified cortical cell types could be discriminated from one another with ∼80% accuracy and from other neurons with ∼85% accuracy. NP Ultra improves yield, detection of subcellular compartments, and cell type identification to enable a more powerful dissection of neural circuit activity during behavior.

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetics and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep learning classifier that predicts cell types with greater than 95% accuracy based on the waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously recorded cell types during behavior.

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density (~10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neuron's electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to ~3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

The mechanistic link between neural circuit activity and behavior remains unclear. While manipulating cortical activity can bias certain behaviors and elicit artificial percepts, some tasks can still be solved when cortex is silenced or removed. Here, mice were trained to perform a visual detection task during which we selectively targeted groups of visually responsive and co-tuned neurons in L2/3 of primary visual cortex (V1) for two-photon photostimulation. The influence of photostimulation was conditional on two key factors: the behavioral state of the animal and the contrast of the visual stimulus. The detection of low-contrast stimuli was enhanced by photostimulation, while the detection of high-contrast stimuli was suppressed, but crucially, only when mice were highly engaged in the task. When mice were less engaged, our manipulations of cortical activity had no effect on behavior. The behavioral changes were linked to specific changes in neuronal activity. The responses of non-photostimulated neurons in the local network were also conditional on two factors: their functional similarity to the photostimulated neurons and the contrast of the visual stimulus. Functionally similar neurons were increasingly suppressed by photostimulation with increasing visual stimulus contrast, correlating with the change in behavior. Our results show that the influence of cortical activity on perception is not fixed, but dynamically and contextually modulated by behavioral state, ongoing activity and the routing of information through specific circuits.

To investigate which activity patterns in sensory cortex are relevant for perceptual decision-making, we combined two-photon calcium imaging and targeted two-photon optogenetics to interrogate barrel cortex activity during perceptual discrimination. We trained mice to discriminate bilateral whisker deflections and report decisions by licking left or right. Two-photon calcium imaging revealed sparse coding of contralateral and ipsilateral whisker input in layer 2/3, with most neurons remaining silent during the task. Activating pyramidal neurons using two-photon holographic photostimulation evoked a perceptual bias that scaled with the number of neurons photostimulated. This effect was dominated by optogenetic activation of non-coding neurons, which did not show sensory or motor-related activity during task performance. Photostimulation also revealed potent recruitment of cortical inhibition during sensory processing, which strongly and preferentially suppressed non-coding neurons. Our results suggest that a pool of non-coding neurons, selectively suppressed by network inhibition during sensory processing, can be recruited to enhance perception.

The neural representations of prior information about the state of the world are poorly understood. To investigate this issue, we examined brain-wide Neuropixels recordings and widefield calcium imaging collected by the International Brain Laboratory. Mice were trained to indicate the location of a visual grating stimulus, which appeared on the left or right with prior probability alternating between 0.2 and 0.8 in blocks of variable length. We found that mice estimate this prior probability and thereby improve their decision accuracy. Furthermore, we report that this subjective prior is encoded in at least 20% to 30% of brain regions which, remarkably, span all levels of processing, from early sensory areas (LGd, VISp) to motor regions (MOs, MOp, GRN) and high level cortical regions (ACCd, ORBvl). This widespread representation of the prior is consistent with a neural model of Bayesian inference involving loops between areas, as opposed to a model in which the prior is incorporated only in decision making areas. This study offers the first brain-wide perspective on prior encoding at cellular resolution, underscoring the importance of using large scale recordings on a single standardized task.

A key challenge in neuroscience is understanding how neurons in hundreds of interconnected brain regions integrate sensory inputs with prior expectations to initiate movements. It has proven difficult to meet this challenge when different laboratories apply different analyses to different recordings in different regions during different behaviours. Here, we report a comprehensive set of recordings from 115 mice in 11 labs performing a decision-making task with sensory, motor, and cognitive components, obtained with 547 Neuropixels probe insertions covering 267 brain areas in the left forebrain and midbrain and the right hindbrain and cerebellum. We provide an initial appraisal of this brain-wide map, assessing how neural activity encodes key task variables. Representations of visual stimuli appeared transiently in classical visual areas after stimulus onset and then spread to ramp-like activity in a collection of mid- and hindbrain regions that also encoded choices. Neural responses correlated with motor action almost everywhere in the brain. Responses to reward delivery and consumption versus reward omission were also widespread. Representations of objective prior expectations were weaker, found in sparse sets of neurons from restricted regions. This publicly available dataset represents an unprecedented resource for understanding how computations distributed across and within brain areas drive behaviour.

Sensory processing in the neocortex requires both feedforward and feedback information flow between cortical areas1. In feedback processing, higher-level representations provide contextual information to lower levels, and facilitate perceptual functions such as contour integration and figure-ground segmentation2,3. However, we have limited understanding of the circuit and cellular mechanisms that mediate feedback influence. Here we use long-range all-optical connectivity mapping in mice to show that feedback influence from the lateromedial higher visual area (LM) to the primary visual cortex (V1) is spatially organized. When the source and target of feedback represent the same area of visual space, feedback is relatively suppressive. By contrast, when the source is offset from the target in visual space, feedback is relatively facilitating. Two-photon calcium imaging data show that this facilitating feedback is nonlinearly integrated in the apical tuft dendrites of V1 pyramidal neurons: retinotopically offset (surround) visual stimuli drive local dendritic calcium signals indicative of regenerative events, and two-photon optogenetic activation of LM neurons projecting to identified feedback-recipient spines in V1 can drive similar branch-specific local calcium signals. Our results show how neocortical feedback connectivity and nonlinear dendritic integration can together form a substrate to support both predictive and cooperative contextual interactions.

Primary sensory cortex is thought to process incoming sensory information, while decision variables important for driving behavior are assumed to arise downstream in the processing hierarchy. Here, we used population two-photon calcium imaging and targeted two-photon optogenetic stimulation of neurons in layer 2/3 of mouse primary somatosensory cortex (S1) during a texture discrimination task to test for the presence of decision signals and probe their behavioral relevance. Small but distinct populations of neurons carried information about the stimulus irrespective of the behavioral outcome (stimulus neurons), or about the choice irrespective of the presented stimulus (decision neurons). Decision neurons show categorical coding that develops during learning, and lack a conclusive decision signal in Miss trials. All-optical photostimulation of decision neurons during behavior improves behavioral performance, establishing a causal role in driving behavior. The fact that stimulus and decision neurons are intermingled challenges the idea of S1 as a purely sensory area, and causal perturbation suggests a direct involvement of S1 decision neurons in the decision-making process.

Recent advances combining two-photon calcium imaging and two-photon optogenetics with digital holography now allow us to read and write neural activity in vivo at cellular resolution with millisecond temporal precision. Such “all-optical” techniques enable experimenters to probe the impact of functionally defined neurons on neural circuit function and behavioural output with new levels of precision. This protocol describes the experimental strategy and workflow for successful completion of typical all-optical interrogation experiments in awake, behaving head-fixed mice. We describe modular procedures for the setup and calibration of an all-optical system, the preparation of an indicator and opsin-expressing and task-performing animal, the characterization of functional and photostimulation responses and the design and implementation of an all-optical experiment. We discuss optimizations for efficiently selecting and targeting neuronal ensembles for photostimulation sequences, as well as generating photostimulation response maps from the imaging data that can be used to examine the impact of photostimulation on the local circuit. We demonstrate the utility of this strategy using all-optical experiments in three different brain areas – barrel cortex, visual cortex and hippocampus – using different experimental setups. This approach can in principle be adapted to any brain area for all-optical interrogation experiments to probe functional connectivity in neural circuits and for investigating the relationship between neural circuit activity and behaviour.