How can we identify the mechanistic origins of the electrophysiological activity that is recorded from neurons in the living brain? A promising strategy for addressing this question is to generate biologically realistic models of in vivo recorded neurons, and simulate how they transform synaptic inputs from the network into their observed neuronal activity. For this purpose, we here provide our approaches for the generation, simulation, and analysis of network-embedded neuron models as an open source, fully documented and freely available software environment: In Silico Framework (ISF). ISF is centered around the concept of achieving “model consensus” about the mechanistic origins of in vivo recorded activity across biologically diverse sets of models. To achieve such model consensus, ISF offers three key workflows. First, ISF enables users to generate models that are equally well constrained by empirical data at subcellular, cellular and network scales, while the set of models as a whole is constructed to exhibit maximally diverse parameters, spanning the full ranges permitted by the empirically observed biological variability at each scale. Second, ISF enables users to identify those subsets of model configurations that predict the in vivo observations without being tuned to do so. Third, for each of those model configurations, ISF enables users to identify which mechanisms at subcellular, cellular and network scales are necessary to predict the in vivo observations, and which mechanisms are dispensable. Thereby, ISF can reveal which mechanisms are common across model configurations, and whether the diversity of model configurations could account for the variability of the in vivo observed activity across animals, cells and trials. In essence, by achieving such model consensus, ISF predicts mechanisms that are robust across biological variability, and which may hence indeed be used in vivo . Finally, ISF enables users to derive model consensus for in silico manipulations, to identify which experimental strategies would be best suited to test the predicted mechanisms in vivo . We exemplify how we have used this iterative in silico - in vivo approach of ISF to dissect the mechanistic origins of sensory responses in the barrel cortex. By making ISF available as a standalone online resource, we believe it will facilitate the generation, simulation and analysis of models that reveal mechanistic origins of in vivo recorded activity beyond the barrel cortex for which it was originally designed.

How can we understand the enormous diversity of the GABAergic inhibitory neurons in the cerebral cortex? To address this question, we quantify the electrophysiological and morphological properties of inhibitory neurons across the depth of an entire cortical column in the rat barrel cortex. We find properties that shift gradually with the cortical depth of the cell bodies across all inhibitory neurons, regardless of their cell types. By isolating morphoelectric variations from their shifts along the cortical depth, we find that the same simple relationships between morphoelectric properties distinguish between the four main molecular cell types of inhibitory neurons at any cortical depth, and in both the rat barrel cortex and mouse primary visual cortex. We provide converging evidence from dense electron-microscopic reconstructions of inhibitory neurons in the mouse visual cortex, and observe comparable depth-dependent shifts in additional datasets from the mouse primary motor cortex and the middle temporal gyrus of the human cortex. Our findings indicate that two different sources of morphoelectric variations can account for the diversity of cortical inhibitory neurons. The first source is molecular cell type-specific, but cortical depth-independent. The second source is cortical depth-dependent, but affects inhibitory neurons similarly across all cell types. We propose that intrinsic developmental specification vs. extrinsic environmental modulation leads to such a decoupling of inhibitory type-specific properties from gradual shifts of these properties with cortical depth.

What is the neural substrate that enables the cerebral cortex to control a single mystacial vibrissa and orchestrate its movement? To answer this question, we injected rabies virus into the intrinsic muscle that protracts the rat C3 vibrissa and used retrograde transneuronal transport to identify the cortical neurons that control the muscle. A surprisingly diverse set of cortical areas is the origin of disynaptic control over the motoneurons that influence the C3 protractor. More than two thirds of these layer 5 pyramidal neurons (L5PNs) are dispersed in frontal and parietal areas outside the primary motor cortex (vM1). This observation emphasizes the importance of descending commands from non-primary motor areas. More than a third of the L5PNs originate from somatosensory areas, such as the barrel field (vS1). The barrel field has been long considered a prototypic model system for studying sensory processing at the level of the cerebral cortex. Even so, we find that the number of L5PNs in vS1, and even their peak density, rivals the number and peak density of L5PNs in vM1. Thus, our results emphasize the importance of the barrel field in processing motor output. The distribution of L5PNs in vM1 and vS1 leads us to propose a new model of vibrissa protraction in which vM1 output results in protraction, and vS1 output results in reciprocal inhibition (suppression) of protraction. This paired initiation and suppression of complementary movements may be a general feature of the descending control signals from the rodent M1 and S1. ### Competing Interest Statement The authors have declared no competing interest.

Dendritic calcium action potentials (APs) enable the main output neurons of the cerebral cortex - pyramidal tract neurons (PTs) - to associate inputs that arrive at different cortical layers. How synaptic inputs evoke calcium APs during in vivo conditions is yet unclear. We combine in vivo recordings in male rats with synaptic input reconstructions, multi-scale modelling and optogenetic manipulations. We find that thalamocortical (TC) synapses, which provide sensory input to cortex, target specifically and most densely the dendritic domain that initiates calcium APs in PTs. Sensory input from thalamus is hence a reliable, but weak source for activating the dendritic calcium domain. Because it is fast and local, this activation enables active dendritic coupling of sensory input with multiple sensory-evoked and ongoing input streams that arrive during and surprisingly before the stimulus. This 'TC coupling' mechanism accounts for the modulation of the first sensory responses that leave the cortex with bursts of APs.

Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse connectivity for a network’s function? Surprisingly, it has been shown that sparse connectivity impairs information processing in artificial neural networks (ANNs). Does this imply that sparse connectivity also impairs information processing in biological neural networks? Although ANNs were originally inspired by the brain, conventional ANNs differ substantially in their structural network architecture from cortical networks. To disentangle the relevance of these structural properties for information processing in networks, we systematically constructed ANNs constrained by interpretable features of cortical networks. We find that in large and recurrently connected networks, as are found in the cortex, sparse connectivity facilitates time- and data-efficient information processing. We explore the origins of these surprising findings and show that conventional dense ANNs distribute information across only a very small fraction of nodes, whereas sparse ANNs distribute information across more nodes. We show that sparsity is most critical in networks with fixed excitatory and inhibitory nodes, mirroring neuronal cell types in cortex. This constraint causes a large learning delay in densely connected networks which is eliminated by sparse connectivity. Taken together, our findings show that sparse connectivity enables efficient information processing given key constraints from cortical networks, setting the stage for further investigation into higher-order features of cortical connectivity.

What is the neural substrate that enables the cerebral cortex to control a single mystacial vibrissa and orchestrate its movement? To answer this question, we injected rabies virus into the intrinsic muscle that protracts the rat C3 vibrissa and used retrograde transneuronal transport to identify the cortical neurons that influence the muscle. A surprisingly diverse set of cortical areas is the origin of disynaptic control over the motoneurons that influence the C3 protractor. More than two thirds of these layer 5 pyramidal neurons (L5PNs) are dispersed in frontal and parietal areas outside the primary motor cortex (vM1). This observation emphasizes the importance of descending motor commands from non-primary motor areas. More than a third of the L5PNs originate from somatosensory areas, such as the barrel field (vS1). The barrel field has been long considered a prototypic model system for studying sensory processing at the level of the cerebral cortex. Even so, we find that the number of L5PNs in vS1, and even their peak density, rivals the number and peak density of L5PNs in vM1. Thus, our results emphasize the importance of the barrel field in processing motor output. The distribution of L5PNs in vM1 and vS1 leads us to propose a model of vibrissa protraction in which vM1 output results in protraction, and vS1 output results in reciprocal inhibition (suppression) of protraction. This paired initiation and suppression of complementary movements may be a general feature of the descending output from the rodent M1 and S1.

The FOXP2/Foxp2 gene, linked to fine motor control in vertebrates, is a potential candidate gene thought to play a prominent role in human language production. It is expressed specifically in a subset of corticothalamic (CT) pyramidal cells (PCs) in layer 6 (L6) of the neocortex. These L6 FOXP2+ PCs project exclusively to the thalamus, with L6a PCs targeting first-order or both first- and higher-order thalamic nuclei, whereas L6b PCs connect only to higher-order nuclei. Synaptic connections established by both L6a and L6b FOXP2+ PCs have low release probabilities and respond strongly to acetylcholine (ACh), triggering action potential (AP) trains. Notably, L6b FOXP2- PCs are more sensitive to ACh than L6a, and L6b FOXP2+ PCs also react robustly to dopamine. Thus, FOXP2 labels L6a and L6b CT PCs, which are precisely regulated by neuromodulators, highlighting their roles as potent modulators of thalamic activity.

Wiring specificity in the cortex is observed across scales from the subcellular to the network level. It describes the deviations of connectivity patterns from those expected in randomly connected networks. Understanding the origins of wiring specificity in neural networks remains difficult as a variety of generative mechanisms could have contributed to the observed connectome. To take a step forward, we propose a generative modeling framework that operates directly on dense connectome data as provided by saturated reconstructions of neural tissue. The computational framework allows testing different assumptions of synaptic specificity while accounting for anatomical constraints posed by neuron morphology, which is a known confounding source of wiring specificity. We evaluated the framework on dense reconstructions of the mouse visual and the human temporal cortex. Our template model incorporates assumptions of synaptic specificity based on cell type, single-cell identity, and subcellular compartment. Combinations of these assumptions were sufficient to model various connectivity patterns that are indicative of wiring specificity. Moreover, the identified synaptic specificity parameters showed interesting similarities between both datasets, motivating further analysis of wiring specificity across species. ### Competing Interest Statement The authors have declared no competing interest.

Astrocytes and oligodendrocytes in the ventrobasal thalamus are electrically coupled through gap junctions. We have previously shown that these cells form large panglial networks, which have a key role in the transfer of energy substrates to postsynapses for sustaining neuronal activity. Here, we show that the efficiency of these transfer networks is regulated by synaptic activity: preventing the generation and propagation of action potentials resulted in reduced glial coupling. Systematic analyses of mice deficient for individual connexin isoforms revealed that oligodendroglial Cx32 and Cx47 are the targets of this modulation. Importantly, we show that during a critical time window, sensory deprivation through whisker trimming reduces the efficiency of the glial transfer networks also in vivo. Together with our previous results the current findings indicate that neuronal activity and provision of energy metabolites through panglial coupling are interdependent events regulated in a bidirectional manner.

Neurons in the cerebral cortex receive thousands of synaptic inputs per second from thousands of presynaptic neurons. How the dendritic location of inputs, their timing, strength, and presynaptic origin, in conjunction with complex dendritic physiology, impact the transformation of synaptic input into action potential (AP) output remains generally unknown for in vivo conditions. Here, we introduce a computational approach to reveal which properties of the input causally underlie AP output, and how this neuronal input-output computation is influenced by the morphology and biophysical properties of the dendrites. We demonstrate that this approach allows dissecting of how different input populations drive in vivo observed APs. For this purpose, we focus on fast and broadly tuned responses that pyramidal tract neurons in layer 5 (L5PTs) of the rat barrel cortex elicit upon passive single whisker deflections. By reducing a multi-scale model that we reported previously, we show that three features are sufficient to predict with high accuracy the sensory responses and receptive fields of L5PTs under these specific in vivo conditions: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on these three features, we derive an analytically tractable description of the input-output computation of L5PTs, which enabled us to dissect how synaptic input from thalamus and different cell types in barrel cortex contribute to these responses. We show that the input-output computation is preserved across L5PTs despite morphological and biophysical diversity of their dendrites. We found that trial-to-trial variability in L5PT responses, and cell-to-cell variability in their receptive fields, are sufficiently explained by variability in synaptic input from the network, whereas variability in biophysical and morphological properties have minor contributions. Our approach to derive analytically tractable models of input-output computations in L5PTs provides a roadmap to dissect network-neuron interactions underlying L5PT responses across different in vivo conditions and for other cell types.

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings. This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

Neurons receive input from thousands of synapses, which they transform into action potentials (APs) via their complex dendrites. How the dendritic location of these inputs, their timing, strength, and presynaptic origin impact AP output remains generally unknown. Here we demonstrate how to reveal which properties of the input causally underlie AP output, and how this input-output computation is influenced by the morphology and biophysical properties of the dendrites. For this purpose, we derive analytically tractable, interpretable models of the input-output computation that layer 5 pyramidal tract neurons (L5PTs) – the major output cell type of the cerebral cortex – perform upon sensory stimulation. We find that this input-output computation is preserved across L5PTs despite morphological and biophysical diversity. We show that three features are sufficient to explain in vivo observed sensory responses and receptive fields of L5PTs with high accuracy: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on this analytically tractable and interpretable description of the input-output computation, we show how to dissect the contributions of different input populations in thalamus and cortex to sensory responses of L5PTs. Thus, our approach provides a roadmap for revealing cellular input-output computations across different in vivo conditions. Author Summary Revealing how synaptic inputs drive action potential output is one of the major challenges in neuroscience research. An increasing number of approaches therefore seek to combine detailed measurements at synaptic, cellular and network scales into biologically realistic brain models. Indeed, such models have started to make empirically testable predictions about the inputs that underlie in vivo observed activity patterns. However, the enormous complexity of these models generally prevents the derivation of interpretable descriptions that explain how neurons transform synaptic input into action potential output, and how these input-output computations depend on synaptic, cellular and network properties. Here we introduce an approach to reveal input-output computations that neurons in the cerebral cortex perform upon sensory stimulation. We reduce a realistic multi-scale cortex model to the minimal description that accounts for in vivo observed responses. Thereby, we identify the input-output computation that these cortical neurons perform under this in vivo condition, and we show that this computation is preserved across neurons despite morphological and biophysical diversity. Our approach provides analytically tractable and interpretable descriptions of neuronal input-output computations during specific in vivo conditions.

Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research.

The neurons in the cerebral cortex are not randomly interconnected. This specificity in wiring can result from synapse formation mechanisms that connect neurons, depending on their electrical activity and genetically defined identity. Here, we report that the morphological properties of the neurons provide an additional prominent source by which wiring specificity emerges in cortical networks. This morphologically determined wiring specificity reflects similarities between the neurons' axo-dendritic projections patterns, the packing density, and the cellular diversity of the neuropil. The higher these three factors are, the more recurrent is the topology of the network. Conversely, the lower these factors are, the more feedforward is the network's topology. These principles predict the empirically observed occurrences of clusters of synapses, cell type-specific connectivity patterns, and nonrandom network motifs. Thus, we demonstrate that wiring specificity emerges in the cerebral cortex at subcellular, cellular, and network scales from the specific morphological properties of its neuronal constituents.

The analysis of brain networks is central to neurobiological research. In this context the following tasks often arise: (1) understand the cellular composition of a reconstructed neural tissue volume to determine the nodes of the brain network; (2) quantify connectivity features statistically; and (3) compare these to predictions of mathematical models. We present a framework for interactive, visually supported accomplishment of these tasks. Its central component, the stratification matrix viewer, allows users to visualize the distribution of cellular and/or connectional properties of neurons at different levels of aggregation. We demonstrate its use in four case studies analyzing neural network data from the rat barrel cortex and human temporal cortex.

Maintaining an appropriate balance between excitation and inhibition is critical for neuronal information pro-cessing. Cortical neurons can cell-autonomously adjust the inhibition they receive to individual levels of excitatory input, but the underlying mechanisms are unclear. We describe that Ste20-like kinase (SLK) me-diates cell-autonomous regulation of excitation-inhibition balance in the thalamocortical feedforward circuit, but not in the feedback circuit. This effect is due to regulation of inhibition originating from parvalbumin-ex-pressing interneurons, while inhibition via somatostatin-expressing interneurons is unaffected. Computa-tional modeling shows that this mechanism promotes stable excitatory-inhibitory ratios across pyramidal cells and ensures robust and sparse coding. Patch-clamp RNA sequencing yields genes differentially regu-lated by SLK knockdown, as well as genes associated with excitation-inhibition balance participating in transsynaptic communication and cytoskeletal dynamics. These data identify a mechanism for cell -autono-mous regulation of a specific inhibitory circuit that is critical to ensure that a majority of cortical pyramidal cells participate in information coding.

The mammalian brain has an enormous demand for energy, which is thought to impose strong selective pressure by which the neurons evolve in ways that ensure robust function at minimal energy cost. However, which principles drive the ion channel distributions in the dendrites to implement different neuronal functions is yet unclear. Here we found that an energy-efficient generation of dendritic calcium action potentials in cortical pyramidal neurons requires a low expression of slow inactivating potassium channels. We demonstrate that this relationship between energy cost and neuronal function is independent of the dendritic morphology and the expression patterns of other ion channels that implement additional perisomatic and dendritic functions. Moreover, we found that calcium action potentials can arise from a wide spectrum of ion channel expression patterns, including configurations with high potassium channel densities in the dendrites. These configurations can account equally well for the characteristic intrinsic physiology of the pyramidal neurons. However, only configurations with low potassium channel densities in the distal dendrites are observed empirically. Thus, our findings indicate that cortical neurons do not utilize all theoretically possible ways to implement their functions, but instead select those optimized for energy-efficient active dendritic computations.

Perception is causally linked to a calcium-dependent spiking mechanism that is built into the distal dendrites of layer 5 pyramidal tract neurons – the major output cell type of the cerebral cortex. It is yet unclear which circuits activate this cellular mechanism upon sensory stimulation. Here we found that the same thalamocortical axons that relay sensory signals to layer 4 also densely target the dendritic domain by which pyramidal tract neurons initiate calcium spikes. Distal dendritic inputs, which normally appear greatly attenuated at the cell body, thereby generate bursts of action potentials in cortical output during sensory processing. Our findings indicate that thalamus gates an active dendritic mechanism to facilitate the combination of sensory signals with top-down information streams into cortical output. Thus, in addition to being the central hub for sensory signals, thalamus is also likely to ensure that the signals it relays to cortex are perceived by the animal.