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Cellular diversification is critical for specialized functions of the brain including learning and memory(1). Single cell RNA-sequencing facilitates transcriptomic profiling of distinct major types of neurons(2-4), but divergence of transcriptomic profiles within a neuronal population and their link to function remain poorly understood. Here, we isolate nuclei tagged(5) in specific cell types followed by single nuclear RNA sequencing to profile Purkinje neurons and map their responses to motor activity and learning. We find that two major subpopulations of Purkinje neurons, identified by the genes Aldoc and Plcb4, bear distinct transcriptomic features. Plcb4+, but not Aldoc+, Purkinje neurons display robust plasticity of gene expression in mice subjected to sensorimotor and learning experience. In vivo calcium imaging and optogenetic perturbation reveal that Plcb4+ Purkinje neurons play a crucial role in associative learning. Integrating single nuclear RNA-seq datasets with weighted gene network analysis uncovers a learning gene module that includes components of FGFR2 signaling in Plcb4+ Purkinje neurons. Knockout of Fgfr2 in Plcb4+ Purkinje neurons in mice by a CRISPR approach disrupts motor learning. Our findings define how diversification of Purkinje neurons links to their responses in motor learning and provide a foundation for understanding their differential vulnerability to neurological disorders.

The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

Perturbation to the cerebellum can lead to motor dysfunction, cognitive deficits, and behavioral inflexibility. Here we report that a cerebellum-specific transgenic mouse model with disrupted Purkinje cell function shows unexpectedly accelerated learning on a sensory evidence-accumulation task, as well as enhanced sensory reactivity to touch and auditory cues. Computational latent-state analysis of behavior revealed that accelerated learning was associated with enhanced focus on current over past trials. Learning was also accelerated by providing cue-locked optogenetic stimulation of Purkinje cells, but unaffected by continuous optogenetic interference with Purkinje cell activity. Both transgenic and optogenetically-boosted mice showed prolonged electrophysiological activity in Purkinje-cell complex spikes and anterior cingulate cortex. We suggest that cerebellar activity may shape evidence-accumulation learning by enhancing task focus and neocortical processing of current experience.

The cerebellum regulates nonmotor behavior, but the routes by which it exerts its influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task, acting through a network of diencephalic and neocortical structures. After chemogenetic inhibition of Purkinje cells in lobule VI or crus I, mice could learn a water Y-maze task but were impaired in their ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic regions and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each test group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into well-separated sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of complex whole-body movement revealed deficiencies in across-day adaptation to an open field environment. Neither perturbation affected gait, within-day open-field adaptation, or location preference. Taken together, these experiments reveal brainwide systems for cerebellar influence that can affect multiple flexible responses.Significance statementThe cerebellum, a part of all vertebrate brains, provides feedback to the rest of the brain on a split-second basis in response to unexpected events. The consequent changes can range from minute adjustments in movement to broad changes in behavior. In mice, we investigated cerebellar regions that help guide flexible behavior. Inactivation of these regions prevented mice from responding flexibly to a changing Y-shaped maze. We used light-sheet microscopy of c-fos protein expression to map brainwide effects of different stages of learning and cerebellar inactivation. Inactivation caused changes in thalamocortical activity that were similar in pattern, but opposite in sign, from normal learning. Lobule VI inactivation weakened thalamic functional networks, while crus I inactivation divided sensorimotor from associative neocortical networks. The same inactivation also impaired multiday adaptation to an open arena, as tracked using machine vision. Our work shows how the cerebellum’s influence over flexible response can be mediated by a widespread brain network.

Calcium imaging with protein-based indicators is widely used to follow neural activity in intact nervous systems. The popular GCaMP indicators are based on the calcium-binding protein calmodulin and the RS20 peptide. These sensors report neural activity at timescales much slower than electrical signaling, limited by their biophysical properties and trade-offs between sensitivity and speed. We used large-scale screening and structure-guided mutagenesis to develop and optimize several fast and sensitive GCaMP-type indicators. The resulting ‘jGCaMP8’ sensors, based on calmodulin and a fragment of endothelial nitric oxide synthase, have ultra-fast kinetics (rise times, 2 ms) and still feature the highest sensitivity for neural activity reported for any protein-based sensor. jGCaMP8 sensors will allow tracking of larger populations of neurons on timescales relevant to neural computation.

Background: Repetitive action, resistance to environmental change, and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here, we aimed at investigating behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice, both mutations are found in the clinical conditions and have been associated with ASD. We observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. Methods: Here, we use advances in computer vision and deep learning, a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple time scales using a single popular behavioral assay, the open field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. Results: Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Limitations: Genetic risk factors for autism are numerous, and here we tested only two. Our pipeline was only applied to conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. Conclusions: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as a quantitative diagnosis criteria.

Development of brain circuitry requires precise regulation and timing of proliferation and differentiation of neural progenitor cells. The p75 neurotrophin receptor (p75NTR) is highly expressed in the proliferating granule cell precursors (GCPs) during development of the cerebellum. In a previous paper, we showed that proNT3 promoted GCP cell cycle exit via p75NTR. Here we used genetically modified rats and mice of both sexes to show that p75NTR regulates the duration of the GCP cell cycle, requiring activation of RhoA. Rats and mice lacking p75NTR have dysregulated GCP proliferation, with deleterious effects on cerebellar circuit development and behavioral consequences persisting into adulthood. In the absence of p75NTR, the GCP cell cycle is accelerated, leading to delayed cell cycle exit, prolonged GCP proliferation, increased glutamatergic input to Purkinje cells, and a deficit in delay eyeblink conditioning, a cerebellum-dependent form of learning. These results demonstrate the necessity of appropriate developmental timing of the cell cycle for establishment of proper connectivity and associated behavior.SIGNIFICANCE STATEMENTThe cerebellum has been shown to be involved in numerous behaviors in addition to its classic association with motor function. Cerebellar function is disrupted in a variety of psychiatric disorders, including those on the autism spectrum. Here we show that the p75 neurotrophin receptor, which is abundantly expressed in the proliferating cerebellar granule cell progenitors, regulates the cell cycle of these progenitors. In the absence of this receptor, the cell cycle is dysregulated, leading to excessive progenitor proliferation, which alters the balance of inputs to Purkinje cells, disrupting the circuitry and leading to functional deficits that persist into adulthood.

Key points Spike doublets comprise ∼10% of in vivo complex spike events under spontaneous conditions and ∼20% (up to 50%) under evoked conditions. Under near-physiological slice conditions, single complex spikes do not induce parallel fibre long-term depression. Doublet stimulation is required to induce long-term depression with an optimal parallel-fibre to first-complex-spike timing interval of 150 ms. Abstract The classic example of biological supervised learning occurs at cerebellar parallel fibre (PF) to Purkinje cell synapses, comprising the most abundant synapse in the mammalian brain. Long-term depression (LTD) at these synapses is driven by climbing fibres (CFs), which fire continuously about once per second and therefore generate potential false-positive events. We show that pairs of complex spikes are required to induce LTD. In vivo, sensory stimuli evoked complex-spike doublets with intervals ≤150 ms in up to 50% of events. Using realistic [Ca2+ ]o and [Mg2+ ]o concentrations in slices, we determined that complex-spike doublets delivered 100-150 ms after PF stimulus onset were required to trigger PF-LTD, which is consistent with the requirements for eyeblink conditioning. Inter-complex spike intervals of 50-150 ms provided optimal decoding. This stimulus pattern prolonged evoked spine calcium signals and promoted CaMKII activation. Doublet activity may provide a means for CF instructive signals to stand out from background firing.

Autism is noted for both its genotypic and phenotypic diversity. Repetitive action, resistance to environmental change, and motor disruptions vary from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we use advances in computer vision and deep learning to develop a framework for characterizing mouse behavior on multiple time scales using a single popular behavioral assay, the open field test. We observed male and female C57BL/6J mice to develop a dynamic baseline of adaptive behavior over multiple days. We then examined two rodent models of autism, a cerebellum-specific model, L7-Tsc1, and a whole-brain knockout model, Cntnap2. Both Cntnap2 knockout and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure-to-adapt took the form of maintained ambling, turning, and locomotion, and an overall decrease in grooming. Adaptation in Cntnap2 knockout mice more broadly resembled that of wild-type. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics.

Democracy often fails to meet its ideals, and these failures may be made worse by electoral institutions. Unwanted outcomes include elite polarization, unresponsive representatives, and the ability of a faction of voters to gain power at the expense of the majority. Various reforms have been proposed to address these problems, but their effectiveness is difficult to predict against a backdrop of complex interactions. Here we outline a path for systems-level modeling to help understand and optimize repairs to US democracy. Following the tradition of engineering and biology, models of systems include mechanisms with dynamical properties that include nonlinearities and amplification (voting rules), positive feedback mechanisms (single-party control, gerrymandering), negative feedback (checks and balances), integration over time (lifetime judicial appointments), and low dimensionality (polarization). To illustrate a systems-level approach, we analyze three emergent phenomena: low dimensionality, elite polarization, and antimajoritarianism in legislatures. In each case, long-standing rules now contribute to undesirable outcomes as a consequence of changes in the political environment. Theoretical understanding at a general level will also help evaluate whether a proposed reform’s benefits will materialize and be lasting, especially as conditions change again. In this way, rigorous modeling may not only shape new lines of research but aid in the design of effective and lasting reform.

Cerebellar outputs take polysynaptic routes to reach the rest of the brain, impeding conventional tracing. Here we quantify pathways between cerebellum and forebrain using transsynaptic tracing viruses and a whole-brain quantitative analysis pipeline. Retrograde tracing found a majority of descending paths originating from somatomotor cortex. Anterograde tracing of ascending paths encompassed most thalamic nuclei, especially ventral posteromedial, lateral posterior, mediodorsal, and reticular nuclei; in neocortex, sensorimotor regions contained the most labeled neurons, but higher densities were found in associative areas, including orbital, anterior cingulate, prelimbic, and infralimbic cortex. Patterns of ascending expression correlated with c-Fos expression after optogenetic inhibition of Purkinje cells. Our results reveal homologous networks linking single areas of cerebellar cortex to diverse forebrain targets. We conclude that shared areas of cerebellum are positioned to provide sensory-motor information to regions implicated in both movement and nonmotor function.Graphical Abstract

Background The preparation consisting of a head-fixed mouse on a spherical or cylindrical treadmill offers unique advantages in a variety of experimental contexts. Head fixation provides the mechanical stability necessary for optical and electrophysiological recordings and stimulation. Additionally, it can be combined with virtual environments such as T-mazes, enabling these types of recording during diverse behaviors. New method In this paper we present a low-cost, easy-to-build acquisition system, along with scalable computational methods to quantitatively measure behavior (locomotion and paws, whiskers, and tail motion patterns) in head-fixed mice locomoting on cylindrical or spherical treadmills. Existing methods Several custom supervised and unsupervised methods have been developed for measuring behavior in mice. However, to date there is no low-cost, turn-key, general-purpose, and scalable system for acquiring and quantifying behavior in mice. Results We benchmark our algorithms against ground truth data generated either by manual labeling or by simpler methods of feature extraction. We demonstrate that our algorithms achieve good performance, both in supervised and unsupervised settings. Conclusions We present a low-cost suite of tools for behavioral quantification, which serve as valuable complements to recording and stimulation technologies being developed for the head-fixed mouse preparation.

A question of legal significance is whether an enacted map of political districts is “typical.” Recent work has used Markov chain Monte Carlo (MCMC) methods to produce null distributions of maps in order to answer this question. A recent article by Cho and Liu critiques one particular implementation of MCMC for redistricting, that of Fifield et al. The goal of the present commentary is to draw attention to two facts omitted by Cho and Liu that, if included, would have severely weakened their conclusions. In particular, Cho and Liu point out that Fifield et al.’s algorithm fails to approximate a known target distribution, but neglect Fifield et al.’s use of parallel and simulated tempering, which greatly improves the approximation. Secondly, Cho and Liu argue that it is overly difficult to detect when Markov chains have mixed; they neglect to mention diagnostics used for this exact purpose in Fifield et al.

SUMMARY Cerebellar outputs take polysynaptic routes to reach the rest of the brain, impeding conventional tracing. Here, we quantify pathways between the cerebellum and forebrain by using transsynaptic tracing viruses and a whole-brain analysis pipeline. With retrograde tracing, we find that most descending paths originate from the somatomotor cortex. Anterograde tracing of ascending paths encompasses most thalamic nuclei, especially ventral posteromedial, lateral posterior, mediodorsal, and reticular nuclei. In the neocortex, sensorimotor regions contain the most labeled neurons, but we find higher densities in associative areas, including orbital, anterior cingulate, prelimbic, and infralimbic cortex. Patterns of ascending expression correlate with c-Fos expression after optogenetic inhibition of Purkinje cells. Our results reveal homologous networks linking single areas of the cerebellar cortex to diverse forebrain targets. We conclude that shared areas of the cerebellum are positioned to provide sensory-motor information to regions implicated in both movement and nonmotor function., Graphical Abstract, In brief Pisano et al. use transsynaptic tracing and whole-brain light-sheet microscopy to quantitatively map cerebellar paths to and from the forebrain, including relatively dense projections to the prefrontal neocortex. Divergence of paths from single injection sites suggests that a single cerebellar region can influence multiple thalamic and neocortical targets at once.

Cerebellar granule cells, which constitute half the brain’s neurons, supply Purkinje cells with contextual information necessary for motor learning, but how they encode this information is unknown. Here we show, using two-photon microscopy to track neural activity over multiple days of cerebellum-dependent eyeblink conditioning in mice, that granule cell populations acquire a dense representation of the anticipatory eyelid movement. Initially, granule cells responded to neutral visual and somatosensory stimuli as well as periorbital airpuffs used for training. As learning progressed, two-thirds of monitored granule cells acquired a conditional response whose timing matched or preceded the learned eyelid movements. Granule cell activity covaried trial by trial to form a redundant code. Many granule cells were also active during movements of nearby body structures. Thus, a predictive signal about the upcoming movement is widely available at the input stage of the cerebellar cortex, as required by forward models of cerebellar control.

Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of Mus musculus) where we track the full conformation of the head, body, and limbs.

To select actions based on sensory evidence, animals must create and manipulate representations of stimulus information in memory. Here we report that during accumulation of somatosensory evidence, optogenetic manipulation of cerebellar Purkinje cells reduces the accuracy of subsequent memory-guided decisions and causes mice to downweight prior information. Behavioral deficits are consistent with the addition of noise and leak to the evidence accumulation process. We conclude that the cerebellum can influence the accurate maintenance of working memory., Disruption of cerebellar activity impairs working memory during evidence accumulation in mice. Here, the authors show that optogenetic perturbation of Purkinje cell activity disrupts the accurate accumulation of somatosensory information in working memory during perceptual decision-making.

Cognitive and social capacities require postnatal experience, yet the pathways by which experience guides development are unknown. Here we show that the normal development of motor and nonmotor capacities requires cerebellar activity. Using chemogenetic perturbation of molecular layer interneurons to attenuate cerebellar output in mice, we found that activity of posterior regions in juvenile life modulates adult expression of eyeblink conditioning (paravermal lobule VI, crus I), reversal learning (lobule VI), persistive behavior and novelty-seeking (lobule VII), and social preference (crus I/II). Perturbation in adult life altered only a subset of phenotypes. Both adult and juvenile disruption left gait metrics largely unaffected. Contributions to phenotypes increased with the amount of lobule inactivated. Using an anterograde transsynaptic tracer, we found that posterior cerebellum made strong connections with prelimbic, orbitofrontal, and anterior cingulate cortex. These findings provide anatomical substrates for the clinical observation that cerebellar injury increases the risk of autism.

To make successful evidence-based decisions, the brain must rapidly and accurately transform sensory inputs into specific goal-directed behaviors. Most experimental work on this subject has focused on forebrain mechanisms. Using a novel evidence-accumulation task for mice, we performed recording and perturbation studies of crus I of the lateral posterior cerebellum, which communicates bidirectionally with numerous forebrain regions. Cerebellar inactivation led to a reduction in the fraction of correct trials. Using two-photon fluorescence imaging of calcium, we found that Purkinje cell somatic activity contained choice/evidence-related information. Decision errors were represented by dendritic calcium spikes, which in other contexts are known to drive cerebellar plasticity. We propose that cerebellar circuitry may contribute to computations that support accurate performance in this perceptual decision-making task.

US political reporting has become extraordinarily rich in polling data. However, this increase in information availability has not been matched by an improvement in the accuracy of poll-based news stories, which usually examine a single survey at a time, rather than providing an aggregated, more accurate view. In 2004, I developed a meta-analysis that reduced the polling noise for the Presidential race by reducing all available state polls to a snapshot at a single time, known as the Electoral Vote estimator. Assuming that Presidential pollsters are accurate in the aggregate, the snapshot has an accuracy equivalent to less than ± 0.5% in the national popular-vote margin. The estimator outperforms both the aggregator FiveThirtyEight and the betting market InTrade. Complex models, which adjust individual polls and employ pre-campaign “fundamental” variables, improve the accuracy in individual states but provide little or no advantage in overall performance, while at the same time reducing transparency. A polls-only snapshot can also identify shifts in the race, with a time resolution of a single day, thus assisting in the identification of discrete events that influence a race. Finally, starting at around Memorial Day, variations in the polling snapshot over time are sufficient to enable the production of a high-quality, random-drift-based prediction without a need for the fundamentals that are traditionally used by political science models. In summary, the use of polls by themselves can capture the detailed dynamics of Presidential races and make predictions. Taken together, these qualities make the meta-analysis a sensitive indicator of the ups and downs of a national campaign—in short, a precise electoral thermometer.

Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of the glutamate sensor iGluSnFR that are functionally brighter; can detect sub-micromolar to millimolar concentrations of glutamate; and have blue, green or yellow emission profiles. These variants allow in vivo imaging where original-iGluSnFR was too dim, reveal glutamate transients at individual spine heads, and permit kilohertz imaging with inexpensive, powerful fiber lasers.

International audience; Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of intensity-based glutamate-sensing fluorescent reporter (iGluSnFR) that are functionally brighter; detect submicromolar to millimolar amounts of glutamate; and have blue, cyan, green, or yellow emission profiles. These variants could be imaged in vivo in cases where original iGluSnFR was too dim, resolved glutamate transients in dendritic spines and axonal boutons, and allowed imaging at kilohertz rates.

Summary Climbing fibers (CFs) are thought to contribute to cerebellar plasticity and learning by triggering a large influx of dendritic calcium in the postsynaptic Purkinje cell (PC) to signal the occurrence of an unexpected sensory event. However, CFs fire about once per second whether or not an event occurs, raising the question of how sensory-driven signals might be distinguished from a background of ongoing spontaneous activity. Here, we report that in PC dendrites of awake mice, CF-triggered calcium signals are enhanced when the trigger is a sensory event. In addition, we show that a large fraction of the total enhancement in each PC dendrite can be accounted for by an additional boost of calcium provided by sensory activation of a non-CF input. We suggest that sensory stimulation may modulate dendritic voltage and calcium concentration in PCs to increase the strength of plasticity signals during cerebellar learning.

Photoactivatable “caged” neurotransmitters allow optical control of neural tissue with high spatial and temporal precision. However, the development of caged versions of the chief vertebrate inhibitory neurotransmitter, γ-amino butyric acid (GABA), has been limited by the propensity of caged GABAs to interact with GABA receptors. We describe herein the synthesis and application of a practically useful doubly caged GABA analog, termed bis-α-carboxy-2-nitrobenzyl-GABA (bis-CNB-GABA). Uncaging of bis-CNB-GABA evokes inward GABAergic currents in cerebellar molecular layer interneurons with rise times of 2 ms, comparable to flash duration. Response amplitudes depend on the square of flash intensity, as expected for a chemical two-photon uncaging effect. Importantly, prior to uncaging, bis-CNB-GABA is inactive at the GABAA receptor, evoking no changes in holding current in voltage-clamped neurons and showing an IC50 of at least 2.5 mM as measured using spontaneous GABAergic synaptic currents. Bis-CNB-GABA is stable in solution, with an estimated half-life of 98 days in the light. We expect that bis-CNB-GABA will prove to be an effective tool for high-resolution chemical control of brain circuits.

In the version of this paper originally published, important figure labels in Fig. 3d were not visible. An image layer present in the authors' original figure that included two small dashed outlines and text labels indicating ROI 1 and ROI 2, as well as a scale bar and the name of the cell label, was erroneously altered during image processing. The figure has been corrected in the HTML and PDF versions of the paper.

The deep cerebellar nuclei (DCN) convey the final output of the cerebellum and are a major site of activity-dependent plasticity. Here, using patch-clamp recording and two-photon calcium imaging in rat brain slices, we demonstrate that DCN dendrites exhibit three hallmarks of active amplification of electrical signals. First, they produce calcium transients with rise times of tens of milliseconds, comparable in amplitude and duration to calcium spikes in other neurons. Second, calcium signal amplitudes are undiminished along the length of dendrites to the farthest distances from the soma. Third, they can generate calcium signals even in the presence of tetrodotoxin, a sodium channel blocker that abolishes somatic action potential initiation. DCN calcium transients do require the action of T-type calcium channels, a common voltage-gated conductance in excitable dendrites. Dendritic calcium influx was evoked by release from hyperpolarization, peaked within tens of milliseconds, and was observed in both transient- and weak-rebound-firing neurons. In a survey across the DCN, transient-burst rebound firing, which was accompanied by the most rapid calcium flux, was more common in lateral nucleus than in interpositus nucleus and was not seen in medial nucleus. Rebound firing and calcium transients were not present in animals shipped 1–3 days before recording, a condition associated with elevated maternal and pup corticosterone and reduced pup body weight. Rebounds could be restored by the protein kinase C activator phorbol 12-myristate-13-acetate. Thus local calcium-based dendritic excitability supports a stage of presomatic amplification that is under regulation by stress and neuromodulatory influence.

Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials in short bursts in several systemsin vivo. Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery ofin vitroassays, we have increased the dynamic range of GCaMP3 by severalfold, creating a family of “GCaMP5” sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, andin vivoinCaenorhabditischemosensory neurons,Drosophilalarval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2- to 3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combiningin vivoimaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activityin vivoand may find widespread applications for cellular imaging in general.

In vivo multiphoton fluorescence microscopy allows imaging of cellular structures in brain tissue to depths of hundreds of micrometers and, when combined with the use of activity-dependent indicator dyes, opens the possibility of observing intact, functioning neural circuitry. We have developed tools for analyzing in vivo multiphoton data sets to identify responding structures and events in single cells as well as patterns of activity within the neural ensemble. Data were analyzed from populations of cerebellar Purkinje cell dendrites, which generate calcium-based complex action potentials. For image segmentation, active dendrites were identified using a correlation-based method to group covarying pixels. Firing events were extracted from dendritic fluorescence signals with a 95% detection rate and an 8% false-positive rate. Because an event that begins in one movie frame is sometimes not detected until the next frame, detection delays were compensated using a likelihood-based correction procedure. To identify groups of dendrites that tended to fire synchronously, a k-means-based procedure was developed to analyze pairwise correlations across the population. Because repeated runs of k-means often generated dissimilar clusterings, the runs were combined to determine a consensus cluster number and composition. This procedure, termed meta- k-means, gave clusterings as good as individual runs of k-means, was independent of random initial seeding, and allowed the exclusion of outliers. Our methods should be generally useful for analyzing multicellular activity recordings in a variety of brain structures.

The brains of large mammals have lower rates of metabolism than those of small mammals, but the functional consequences of this scaling are not well understood. An attractive target for analysis is axons, whose size, speed and energy consumption are straightforwardly related. Here we show that from shrews to whales, the composition of white matter shifts from compact, slow-conducting, and energetically expensive unmyelinated axons to large, fast-conducting, and energetically inexpensive myelinated axons. The fastest axons have conduction times of 1–5 ms across the neocortex and

Like electrical wires, axons carry signals from place to place. However, unlike wires, because of the electrochemical mechanisms for generating and propagating action potentials, the performance of an axon is strongly linked to the costs of its construction and operation. As a consequence, the architecture of brain wiring is biophysically constrained to trade off speed and energetic efficiency against volume. Because the biophysics of axonal conduction is well studied, this tradeoff is amenable to quantitative analysis. In this framework, an examination of axon tract composition can yield insights into neural circuit function in regard to energetics, processing speed, spike timing precision, and average rates of neural activity.