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Understanding the brain’s perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain’s nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models—capable of predicting large-scale neuronal activity in response to arbitrary sensory input—can be powerful tools for characterizing neuronal representations by enabling high-throughputin silicoexperiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models—trained on vast quantities of data— have demonstrated remarkable capabilities and generalization, we developed a “foundation model” of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ∼ 1mm3volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended “inception loops” — a paradigm that iterates between large-scale recordings, neural predictive models, andin silicoexperiments followed byin vivoverification — to systematically characterize single neuron invariances in the mouse primary visual cortex. Using the predictive model we synthesized Diverse Exciting Inputs (DEIs), a set of inputs that differ substantially from each other while each driving a target neuron strongly, and verified these DEIs’ efficacyin vivo. We discovered a novel bipartite invariance: one portion of the receptive field encoded phase-invariant texturelike patterns, while the other portion encoded a fixed spatial pattern. Our analysis revealed that the division between the fixed and invariant portions of the receptive fields aligns with object boundaries defined by spatial frequency differences present in highly activating natural images. These findings suggest that bipartite invariance might play a role in segmentation by detecting texture-defined object boundaries, independent of the phase of the texture. We also replicated these bipartite DEIs in the functional connectomics MICrONs data set, which opens the way towards a circuit-level mechanistic understanding of this novel type of invariance. Our study demonstrates the power of using a data-driven deep learning approach to systematically characterize neuronal invariances. By applying this method across the visual hierarchy, cell types, and sensory modalities, we can decipher how latent variables are robustly extracted from natural scenes, leading to a deeper understanding of generalization.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present “NEURD”, a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

A key role of sensory processing is integrating information across space. Neuronal responses in the visual system are influenced by both local features in the receptive field center and contextual information from the surround. While center-surround interactions have been extensively studied using simple stimuli like gratings, investigating these interactions with more complex, ecologically-relevant stimuli is challenging due to the high dimensionality of the stimulus space. We used large-scale neuronal recordings in mouse primary visual cortex to train convolutional neural network (CNN) models that accurately predicted center-surround interactions for natural stimuli. These models enabled us to synthesize surround stimuli that strongly suppressed or enhanced neuronal responses to the optimal center stimulus, as confirmed byin vivoexperiments. In contrast to the common notion that congruent center and surround stimuli are suppressive, we found that excitatory surrounds appeared to complete spatial patterns in the center, while inhibitory surrounds disrupted them. We quantified this effect by demonstrating that CNN-optimized excitatory surround images have strong similarity in neuronal response space with surround images generated by extrapolating the statistical properties of the center, and with patches of natural scenes, which are known to exhibit high spatial correlations. Our findings cannot be explained by theories like redundancy reduction or predictive coding previously linked to contextual modulation in visual cortex. Instead, we demonstrated that a hierarchical probabilistic model incorporating Bayesian inference, and modulating neuronal responses based on prior knowledge of natural scene statistics, can explain our empirical results. We replicated these center-surround effects in the multi-area functional connectomics MICrONS dataset using natural movies as visual stimuli, which opens the way towards understanding circuit level mechanism, such as the contributions of lateral and feedback recurrent connections. Our data-driven modeling approach provides a new understanding of the role of contextual interactions in sensory processing and can be adapted across brain areas, sensory modalities, and species.

To understand how the neocortex underlies our ability to perceive, think, and act, it is important to study the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron’s tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron’s receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the “like-to-like” connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. The extent to which the morphological diversity of excitatory neurons forms a continuum or is built from distinct clusters of cell types remains an open question. Here we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological “bar code” describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from a millimeter scale serial-section electron microscopy volume. We found a set of principles that captured the morphological diversity of the dendrites of excitatory neurons. First, their morphologies varied with respect to three major axes: soma depth, total apical and basal skeletal length. Second, neurons in layer 2/3 showed a strong trend of a decreasing width of their dendritic arbor and a smaller tuft with increasing cortical depth. Third, in layer 4, atufted neurons were primarily located in the primary visual cortex, while tufted neurons were more abundant in higher visual areas. Fourth, we discovered layer 4 neurons in V1 on the border to layer 5 which showed a tendency towards avoiding deeper layers with their dendrites. In summary, excitatory neurons exhibited a substantial degree of dendritic morphological variation, both within and across cortical layers, but this variation mostly formed a continuum, with only a few notable exceptions in deeper layers.

Metamers, images that are perceived as equal, are a useful tool to study representations of natural images in biological and artificial vision systems. We synthesized metamers for the mouse visual system by inverting a deep encoding model to find an image that matched the observed neural activity to the original presented image. When testing the resulting images in physiological experiments we found that they most closely reproduced the neural activity of the original image when compared to other decoding methods, even when tested in a different animal whose neural activity was not used to produce the metamer. This demonstrates that deep encoding models do capture general characteristic properties of biological visual systems and can be used to define a meaningful perceptual loss for the visual system.

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. While many morphological surveys focus on branching patterns of individual cells, fewer have been devoted to sub-cellular structure of cells. Electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. Here we present the first systematic survey of the somatic region of nearly 100,000 cortical cells, using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to recapitulate many known aspects of cortical organization, while also revealing novel relationships. Parameters of cell size, nuclear infolding and somatic synaptic innervation co-vary with distinct patterns across depth and between types. Further, we describe how these subcellular features can be used to create highly accurate predictions of cell-types across large scale EM datasets. More generally, our results suggest that the shifts in cellular physiology and molecular programming seen across cell types accompany profound differences in the fine-scale structure of cells.

3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach has been limited to small chunks of mammalian brains. To scale up to larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, a popular form of 3D EM. The pipeline employs convolutional nets to compute the nonsmooth transformations required to align images of serial sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as axon, dendrite, soma, and other semantic categories, and detect synapses and assign them to presynaptic and postsynaptic segments. The output of neuronal boundary detection is segmented by mean affinity agglomeration with semantic and size constraints. Pipeline operations are implemented by leveraging distributed and cloud computing. Intermediate results of the pipeline are held in cloud storage, and can be effortlessly viewed as images, which aids debugging. We applied the pipeline to create an automated reconstruction of an EM image volume spanning four visual cortical areas of a mouse brain. Code for the pipeline is publicly available, as is the reconstructed volume.

To understand the brain we must relate neurons’ functional responses to the circuit architecture that shapes them. Here, we present a large functional connectomics dataset with dense calcium imaging of a millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm) in an awake mouse viewing natural movies and synthetic stimuli. The functional data were co-registered with a volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells and 0.5 billion synapses. Subsequent proofreading of a subset of neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Here, we release this dataset as an open-access resource to the scientific community including a set of tools that facilitate data retrieval and downstream analysis. In accompanying papers we describe our findings using the dataset to provide a comprehensive structural characterization of cortical cell types1–3and the most detailed synaptic level connectivity diagram of a cortical column to date2, uncovering unique cell-type specific inhibitory motifs that can be linked to gene expression data4. Functionally, we identify new computational principles of how information is integrated across visual space5, characterize novel types of neuronal invariances6and bring structure and function together to decipher a general principle that wires excitatory neurons within and across areas7, 8.

BackgroundA full understanding of circuits and cellular mechanisms governing health and disease requires the dissection and multi-faceted study of discrete cell subtypes in developing and adult animal models. Recombinase-driven expression of transgenic response alleles represents a significant and powerful approach to delineate cell populations for functional, molecular, and anatomical study. In addition to single recombinase systems, the expression of two recombinases in distinct, but partially overlapping, populations allow for more defined target expression. Although the application of this method is becoming increasingly popular, the expense and difficulty associated with production of customized intersectional mouse lines have limited widespread application to more common allele manipulations that are often commercially produced at great expense.ResultsWe present a simplified CRISPR toolkit for rapid, inexpensive, and facile intersectional allele production. Briefly, we produced 7 intersectional mouse lines using a dual recombinase system, one mouse line with a single recombinase system, and three embryonic stem (ES) cell lines that are designed to study how functional, molecular, and anatomical features relate to each other in building circuits that underlie physiology and behavior. As a proof-of-principle, we applied three of these lines to different neuronal populations for anatomical mapping and functional in vivo investigation of respiratory control. We also generated a mouse line with a single recombinase-responsive allele that controls the expression of the calcium sensor Twitch-2B. This mouse line was applied globally to study the effects of follicle stimulating hormone (FSH) and luteinizing hormone (LH) on calcium release in the ovarian follicle.ConclusionsLines presented here are representative examples of outcomes possible with the successful application of our genetic toolkit for the facile development of diverse, modifiable animal models. This toolkit will allow labs to create single or dual recombinase effector lines easily for any cell population or subpopulation of interest when paired with the appropriate Cre and FLP recombinase mouse lines or viral vectors. We have made our tools and derivative intersectional mouse and ES cell lines openly available for non-commercial use through publicly curated repositories for plasmid DNA, ES cells, and transgenic mouse lines.

In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system.

In mammalian ovarian follicles, follicle stimulating hormone (FSH) and luteinizing hormone (LH) signal primarily through the G-protein G(s) to elevate cAMP, but both of these hormones can also elevate Ca2+ under some conditions. Here, we investigate FSH- and LH-induced Ca2+ signaling in intact follicles of mice expressing genetically encoded Ca2+ sensors, Twitch-2B and GCaMP6s. At a physiological concentration (1 nM), FSH elevates Ca2+ within the granulosa cells of preantral and antral follicles. The Ca2+ rise begins several minutes after FSH application, peaks at similar to 10 min, remains above baseline for another similar to 10 min, and depends on extracellular Ca2+. However, suppression of the FSH-induced Ca2+ increase by reducing extracellular Ca2+ does not inhibit FSH-induced phosphorylation ofMAP kinase, estradiol production, or the acquisition of LH responsiveness. Like FSH, LH also increases Ca2+, when applied to preovulatory follicles. At a physiological concentration (10 nM), LH elicits Ca2+ oscillations in a subset of cells in the outer mural granulosa layer. These oscillations continue for at least 6 h and depend on the activity of G(q) family G-proteins. Suppression of the oscillations by G(q) inhibition does not inhibit meiotic resumption, but does delay the time to 50% ovulation by about 3 h. In summary, both FSH and LH increase Ca2+ in the granulosa cells of intact follicles, but the functions of these Ca2+ rises are only starting to be identified. Summary Sentence Both FSH and LH increase Ca2+ in the granulosa cells of intact ovarian follicles from mice expressing genetically encoded sensors.

Combining two-photon calcium imaging (2PCI) and electron microscopy (EM) provides arguably the most powerful current approach for connecting function to structure in neural circuits. Recent years have seen dramatic advances in obtaining and processing CI and EM data separately. In addition, several joint CI-EM datasets (with CI performed in vivo, followed by EM reconstruction of the same volume) have been collected. However, no automated analysis tools yet exist that can match each signal extracted from the CI data to a cell segment extracted from EM; previous efforts have been largely manual and focused on analyzing calcium activity in cell bodies, neglecting potentially rich functional information from axons and dendrites. There are two major roadblocks to solving this matching problem: first, dense EM reconstruction extracts orders of magnitude more segments than are visible in the corresponding CI field of view, and second, due to optical constraints and non-uniform brightness of the calcium indicator in each cell, direct matching of EM and CI spatial components is nontrivial.In this work we develop a pipeline for fusing CI and densely-reconstructed EM data. We model the observed CI data using a constrained nonnegative matrix factorization (CNMF) framework, in which segments extracted from the EM reconstruction serve to initialize and constrain the spatial components of the matrix factorization. We develop an efficient iterative procedure for solving the resulting combined matching and matrix factorization problem and apply this procedure to joint CI-EM data from mouse visual cortex. The method recovers hundreds of dendritic components from the CI data, visible across multiple functional scans at different depths, matched with densely-reconstructed three-dimensional neural segments recovered from the EM volume. We publicly release the output of this analysis as a new gold standard dataset that can be used to score algorithms for demixing signals from 2PCI data. Finally, we show that this database can be exploited to (1) learn a mapping from 3d EM segmentations to predict the corresponding 2d spatial components estimated from CI data, and (2) train a neural network to denoise these estimated spatial components. This neural network denoiser is a stand-alone module that can be dropped in to enhance any existing 2PCI analysis pipeline.

Neocortical feedback is critical for processes like attention, prediction, and learning. A mechanistic understanding of its function requires deciphering its cell-type wiring logic. Recent studies revealed a disinhibitory circuit between motor and sensory areas in mice, where feedback preferentially targets vasointestinal peptide-expressing interneurons, in addition to pyramidal cells. It is unknown whether this circuit motif is a general cortico-cortical feedback organizing principle. Combining multiple simultaneous whole-cell recordings with optogenetics we found that in contrast to this wiring rule, feedback between the hierarchically organized visual areas (lateral-medial to V1) preferentially activated somatostatin-expressing interneurons. Functionally, both feedback circuits temporally sharpened feed-forward excitation by eliciting a transient increase followed by a prolonged decrease in pyramidal firing rate under sustained feed-forward input. However, under feed-forward transient input, the motor-sensory feedback facilitated pyramidal cell bursting while visual feedback increased spike time precision. Our findings argue for multiple feedback motifs implementing different dynamic non-linear operations.

In primates and most carnivores, neurons in primary visual cortex are spatially organized by their functional properties. For example, neurons with similar orientation preferences are grouped together in iso-orientation domains that smoothly vary over the cortical sheet. In rodents, on the other hand, neurons with different orientation preferences are thought to be spatially intermingled, a feature which has been termed “salt-and-pepper” organization. The apparent absence of any systematic structure in orientation tuning has been considered a defining feature of the rodent visual system for more than a decade, with broad implications for brain development, visual processing, and comparative neurophysiology. Here, we revisited this question using new techniques for wide-field two-photon calcium imaging that enabled us to collect nearly complete population tuning preferences in layers 2-4 across a large fraction of the mouse visual hierarchy. Examining the orientation tuning of these hundreds of thousands of neurons, we found a global map spanning multiple visual cortical areas in which orientation bias was organized around a single pinwheel centered in V1. This pattern was consistent across animals and cortical depth. The existence of this global organization in rodents has implications for our understanding of visual processing and the principles governing the ontogeny and phylogeny of the visual cortex of mammals.

SummaryExcitatory neurons arising from a common progenitor establish radially-oriented clonal units in the neocortex which have been proposed to serve as elementary information processing modules. To characterize the cell types and circuit diagram within these clonal units, we performed single-cell RNA-sequencing and multi-cell patch clamp recordings of neurons derived fromNestin-positive progenitors. We found that radial clones do not appear to be fate-restricted, but instead individual clones are composed of a random sampling of the transcriptomic cell types present in a particular cortical area. The effect of lineage on synaptic connectivity depends on the type of connection tested: pairs of clonally related neurons were more likely to be connected vertically, across cortical layers, but not laterally within the same layer, compared to unrelated pairs. We propose that integration of vertical input from related neurons with lateral input from unrelated neurons may represent a developmentally programmed motif for assembling neocortical circuits.

In mammalian ovarian follicles, follicle stimulating hormone (FSH) and luteinizing hormone (LH) signal primarily through the G-protein Gs to elevate cAMP, but both of these hormones can also elevate Ca2+ under some conditions. Here we investigate FSH- and LH-induced Ca2+ signaling in intact follicles of mice expressing genetically encoded Ca2+ sensors, Twitch-2B and GCaMP6s. At a physiological concentration (1 nM), FSH elevates Ca2+ within the granulosa cells of preantral and antral follicles. The Ca2+ rise begins several minutes after FSH application, peaks at ~10 minutes, remains above baseline for another ~10 minutes, and depends on extracellular Ca2+. However, suppression of the FSH-induced Ca2+ increase by reducing extracellular Ca2+ does not inhibit FSH-induced phosphorylation of MAP kinase, estradiol production, or the acquisition of LH responsiveness. Like FSH, LH also increases Ca2+, when applied to preovulatory follicles. At a physiological concentration (10 nM), LH elicits Ca2+ oscillations in a subset of cells in the outer mural granulosa layer. These oscillations continue for at least 6 hours and depend on the activity of Gq family G-proteins. Suppression of the oscillations by Gq inhibition does not inhibit meiotic resumption, but does delay the time to 50% ovulation by about 3 hours. In summary, both FSH and LH increase Ca2+ in the granulosa cells of intact follicles, but the functions of these Ca2+ rises are only starting to be identified.Summary sentenceBoth FSH and LH increase Ca2+ in the granulosa cells of intact ovarian follicles from mice expressing genetically encoded sensors.

Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed ‘inception loops’, a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli—most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation. The authors develop a deep learning approach that enables an efficient search of the input space to find the best stimuli for modeled neurons. When tested, these stimuli are most effective at driving their matching cells in the brain.

Much of our knowledge about sensory processing in the brain is based on quasi-linear models and the stimuli that optimally drive them. However, sensory information processing is nonlinear, even in primary sensory areas, and optimizing sensory input is difficult due to the high-dimensional input space. We developed inception loops, a closed-loop experimental paradigm that combines in vivo recordings with in silico nonlinear response modeling to identify the Most Exciting Images (MEIs) for neurons in mouse V1. When presented back to the brain, MEIs indeed drove their target cells significantly better than the best stimuli identified by linear models. The MEIs exhibited complex spatial features that deviated from the textbook ideal of V1 as a bank of Gabor filters. Inception loops represent a widely applicable new approach to dissect the neural mechanisms of sensation.

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large, which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are effective when good initializations are available, but can break down in low-SNR settings where common initialization approaches fail. Here we introduce an approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the data., 36 pages, 18 figures

O'Herron et al. (2016) perform two-photon imaging of vascular and neural responses in cat and rodent primary visual cortex to investigate the limits of neurovascular coupling. Their results suggest important constraints on making inferences about neuronal responses from hemodynamic activity.

MECP2-duplication syndrome is an X-linked form of syndromic autism caused by genomic duplication of the region encoding Methyl-CpG-binding protein 2. Mice overexpressing MECP2 demonstrate altered patterns of learning and memory, including enhanced motor learning. Previous work associated this enhanced motor learning to abnormally increased stability of dendritic spine clusters formed in the apical tuft of corticospinal, area M1, neurons during rotarod training. In the current study, we measure the structural plasticity of axonal boutons in Layer 5 (L5) pyramidal neuron projections to layer 1 of area M1 during motor learning. In wild-type mice we find that during rotarod training, bouton formation rate changes minimally, if at all, while bouton elimination rate doubles. Notably, the observed upregulation in bouton elimination with learning is absent in MECP2-duplication mice. This result provides further evidence of imbalance between structural stability and plasticity in this form of syndromic autism. Furthermore, the observation that axonal bouton elimination doubles with motor learning in wild-type animals contrasts with the increase of dendritic spine consolidation observed in corticospinal neurons at the same layer. This dissociation suggests that different area M1 microcircuits may manifest different patterns of structural synaptic plasticity during motor learning.SIGNIFICANCE STATEMENTAbnormal balance between synaptic stability and plasticity is a feature of several autism spectrum disorders, often corroborated by in vivo studies of dendritic spine turnover. Here we provide the first evidence that abnormally increased stability of axonal boutons, the presynaptic component of excitatory synapses, occurs during motor learning in the MECP2 duplication syndrome mouse model of autism. In contrast, in normal controls, axonal bouton elimination in L5 pyramidal neuron projections to layer 1 of area M1 doubles with motor learning. The fact that axonal projection boutons get eliminated, while corticospinal dendritic spines get consolidated with motor learning in layer 1 of area M1, suggests that structural plasticity manifestations differ across different M1 microcircuits.

The G-protein coupled receptor, metabotropic glutamate receptor 5 (mGluR5), is expressed on both cell surface and intracellular membranes in striatal neurons. Using pharmacological tools to differentiate membrane responses, we previously demonstrated that cell surface mGluR5 triggers rapid, transient cytoplasmic Ca(2+) rises, resulting in c-Jun N-terminal kinase, Ca(2+)/calmodulin-dependent protein kinase, and cyclic adenosine 3',5'-monophosphate-responsive element-binding protein (CREB) phosphorylation, whereas stimulation of intracellular mGluR5 induces long, sustained Ca(2+) responses leading to the phosphorylation of extracellular signal-regulated kinase (ERK1/2) and Elk-1 (Jong, Y. J., Kumar, V., and O'Malley, K. L. (2009) J. Biol. Chem. 284, 35827-35838). Using pharmacological, genetic, and bioinformatics approaches, the current findings show that both receptor populations up-regulate many immediate early genes involved in growth and differentiation. Activation of intracellular mGluR5 also up-regulates genes involved in synaptic plasticity including activity-regulated cytoskeletal-associated protein (Arc/Arg3.1). Mechanistically, intracellular mGluR5-mediated Arc induction is dependent upon extracellular and intracellular Ca(2+) and ERK1/2 as well as calmodulin-dependent kinases as known chelators, inhibitors, and a dominant negative Ca(2+)/calmodulin-dependent protein kinase II construct block Arc increases. Moreover, intracellular mGluR5-induced Arc expression requires the serum response transcription factor (SRF) as wild type but not SRF-deficient neurons show this response. Finally, increased Arc levels due to high K(+) depolarization is significantly reduced in response to a permeable but not an impermeable mGluR5 antagonist. Taken together, these data highlight the importance of intracellular mGluR5 in the cascade of events associated with sustained synaptic transmission.