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Is our current knowledge about the structural connectivity of the brain compatible with the measured activity? Using a large-scale spiking network model of leaky integrate-and-fire neurons to achieve simulations with the full neuron and synapse density, we previously answered this question in the affirmative for macaque cortex [1,2]. Here, we apply the same framework to investigate human cortex. Concretely, we present a large-scale spiking network model that relates the cortical network structure to the resting-state activity of neurons, populations, layers, and areas.The construction of the model is based on the integration of data on cortical architecture, single-cell properties, and local and cortico-cortical connectivity into a consistent multi-scale framework. It predicts connection probabilities between any two neurons based on their types and locations within areas and layers. Every area is represented by a 1 mm² microcircuit with area-specific architecture and the full density of neurons and synapses. The cortical architecture in terms of laminar thicknesses and neuron densities is taken from the von Economo and Koskinas atlas [3] and enriched with more detailed data extracted from the BigBrain atlas [4]. While connectivity on the area level is informed by DTI data [5], it is necessary to complement this with predictions on laminar connectivity patterns. We rely on predictive connectomics based on macaque data which express regularities of laminar connectivity patterns as a function of cortical architecture. The local connectivity uses the model by Potjans and Diesmann [6] as a blueprint and is scaled according to the cytoarchitectonic data. Analysis of human neuron morphologies provides synapse-to-soma mappings based on layer- and cell-type-specific dendritic lengths [7]. The model contains roughly 4 million neurons and 50 billion synapses and is simulated on a supercomputer using the NEST simulator.While the available data constrain the parameter space to some extent, the model remains underdetermined. Mean-field theory guides the exploration of the parameter space in search for a low-rate asynchronous irregular state that generates substantial inter-area interactions through cortico-cortical weights that poise the network at the edge of stability. Different realizations of the model are assessed via comparison with experimental data. The simulated functional connectivity is compared with experimental resting-state fMRI data. Furthermore, simulated spiking data is compared with spike recordings from medial frontal cortex recorded in epileptic patients [8]. Preliminary results show that the model can reproduce an asynchronous irregular network state and functional connectivity similar to the resting-state fMRI data. The model serves as a basis for the investigation of multi-scale structure-dynamics relationships in human cortex.AcknowledgmentsFunding: DFG SPP 2041, HBP SGA3 (grant 945539). Compute time: grant JINB33.[1] Schmidt M et al. (2018) Brain Struct Func 223(3), 1409.[2] Schmidt M et al. (2018) PLOS Comp Biol 14(10), e1006359.[3] Von Economo C (2009) Cellular Structure of the Human Cerebral Cortex.[4] Wagstyl K et al. (2020) PLOS Biol 18(4), e3000678.[5] Van Essen DC et al. (2013) NeuroImage 80, 62.[6] Potjans TC, Diesmann M (2014) Cereb Cortex 24(3), 785.[7] Mohan H et al. (2015) Cereb Cortex 25(12), 4839.[8] Minxha J et al. (2020) Science 368(6498).

Given the usefulness of the BigBrain high resolution histological volume, it would be very valuable to have a similar public domain resource for the macaque. Isotropic high resolution data to build such a resource is not currently available, but as an initial step we present a histological volume based on the NIH Blueprint Non-Human Primate (NHP) Atlas [1], acquired by the Allen Institute and funded by the NIH. Our contribution is to take the set of Nissl stained slices (50 μm thick, 250 μm apart) and to create a smooth volume by calculating the optimal alignment. We used open tools [2, 3] for processing, modernized the code of the poSSum three-dimensional reconstruction toolbox [4] and extended the deformable registration method thereof, using the an MRI-based macaque atlas [5] as a reference template. We will use the reconstructed volume to estimate neuron densities across all cortical areas used in the core-nets.org macaque connectivity database [6]. Besides adding to the body of knowledge on the cytoarchitecture [7] and geometry of the macaque cortex, this work will provide tools to further analyze histological data and support large-scale dynamical modeling studies.[1] NIH Blueprint NHP Atlas, www.blueprintnhpatlas.org[2] Bakker et al. Neuroinformatics, 2015[3] Microdraw, microdraw.pasteur.fr[4] Majka et al. Journal of Comparative Neurology, 2016[5] Calabrese et al. NeuroImage, 2015[6] Markov et al. Cerebral Cortex, 2012[7] Beul and Hilgetag. NeuroImage, 2019

Multi-area model of macaque cortex as a scaffold model and workflow test caseAnno Kurth, Alexander van Meegen, Aitor Morales-Gregorio, Jari Pronold, Agnes Korcsak-Gorzo, Hannah Vollenbröker, Rembrandt Bakker, Markus Diesmann and Sacha J van AlbadaThere are many open questions on the relationships between the structure, dynamics and function of the brain, especially from a multi-modal perspective bridging micro-, meso- and macroscopic scales. Large-scale point neuron network models of cortical areas and their interconnections, integrating vast bodies of anatomical data, provide researchers with tools to investigate these issues. In order to make reliable steps in understanding, we need to take an incremental approach to the design of the models, and ensure that they can be built on by others.Here we present a multi-area model (MAM) describing all 32 areas of the macaque vision-related cortex [1] that serves as a scaffold for relating brain structure to its dynamics and function on multiple scales. The model connectivity is determined by processing available anatomical data into a layer-resolved connectome [2] of macaque vision-related cortex. A spiking neural network with the specified connectivity is constructed using NEST [3] and simulated on a supercomputer to study resting-state activity.The model is being extended and refined in various directions: In one project, the motor-related cortical areas are being added, thereby enabling the study of visuo-motor integration in a unified, biologically realistic framework. Mechanisms of spatial attention are being implemented as a first step towards modeling visual processing. Moreover, ongoing work explores the possibility of endowing the anatomically based model with information processing capabilities through learning methods for spiking neural networks [4]. The methods devised to create the macaque model are further generalized to construct a model of the human visual cortex taking into account different neuron characteristics [5] and different anatomical constraints obtained via diffusion imaging [6].Finally, a fully digitized illustrative workflow is provided alongside the MAM to ensure reproducibility and enable re-use by the community. All code is available on GitHub. The tool Snakemake [7] provides a reproducible and user-friendly framework for the execution of the model. The workflow from the anatomical data to the simulation code, analysis and visualization can serve as an example for similar data-driven brain models.[1] Schmidt, M., Bakker, R., Shen, K., Bezgin, G., Diesmann, M., & van Albada, S. J. (2018). A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLOS Computational Biology, 14(10).[2] Schmidt, M., Bakker, R., Hilgetag, C. C., Diesmann, M., & van Albada, S. J. (2018). Multi-scale account of the network structure of macaque visual cortex. Brain Structure and Function, 223(3), 1409-1435.[3] Peyser, A. et al. (2017). NEST 2.14.0. Zenodo. 10.5281/zenodo.882971. [4] Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W. (2018). Long short-term memory and learning-to-learn in networks of spiking neurons. Advances in Neural Information Processing Systems, 787-797.[5] Teeter, C., et al. (2018). Generalized leaky integrate-and-fire models classify multiple neuron types. Nature Communications, 9, 709.[6] Van Essen, D. C., et al. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62-79.[7] Köster, J., & Rahmann, S. (2012). Snakemake—a scalable bioinformatics workflow engine. Bioinformatics, 28(19), 2520-2522.

Understanding the wiring of the brain at the micro-, meso- and macroscale and its influence onneuronal activity is a fundamental problem in neuroscience. Here we present a multi-scale spikingnetwork model of all vision related areas of macaque cortex [1] using the NEST simulator andoutline how we aim to simulate human visual cortex.The connectivity map in our model of the macaque visual cortex integrates data on corticalarchitecture and axonal tracing data into a consistent multi-scale framework and predicts theconnection probability between any two neurons based on their types and locations within areas andlayers [1]. Simulations using this connectivity map reveal a stable asynchronous irregular groundstate with heterogeneous activity across areas, layers and populations [2]. The model of humanvisual cortex will make use of this framework, replacing neuron densities, laminar thicknesses, andcortico-cortical connectivity by estimates for the human brain. To set up the framework, we willfirst model a full cortical hemisphere using published data on cortical architecture [3]. Human-macaque homologies and DTI data will provide reference values for comparison of the cortico-cortical connectivity map. These models will help to elucidate how detailed connectivity of cortexshapes its dynamics on multiple scales and how prominent features of cortical activity can beexplained by population-level connectivity.

Introduction. Visuomotor interactions in the cortex of higher mammals remain poorly understood. Toexplore the relationship of visuomotor dynamics to the underlying network structure we are developinga layer-resolved multi-area model of all vision- and motor-related cortical areas of macaque. The modelextends an existing visual multi-area model [1,2], which represents each cortical area by a full-densitymodel of a cortical microcircuit [3].Motor cortex microcircuit. Motor areas differ crucially from visual cortex: they have a less prominentlayer 4, a far lower neuron density and different internal connectivity. Therefore, we are working on the pa-rameterization of a new motor microcircuit. The new microcircuit will incorporate the available experimentaldata for layer-resolved neuron density, internal connectivity and subcortical connectivity. Since not all pa-rameters have been measured for the macaque motor cortex, we rely on data from other species (mouse,rat, cat) and other cortical areas (somatosensory, parietal), especially for the inter-layer connectivity. Toovercome the limitations of the structural measurements, we will use a genetic algorithm to optimize themodel parameters within biological ranges. The optimization algorithm will maximize the similarity betweenthe simulated activity and layer-resolved resting-state electrophysiological recordings from macaque motorcortex, under a validation framework [4]. The validation procedure will enable the assessment of the modelsensitivity and help determine which parameter families lead to similar dynamics.Full-scale multi-area model. Microcircuits optimized to represent the different motor areas will be in-tegrated into the visual multi-area model to obtain an extended visuomotor model. We will use axonaltract-tracing data to describe the long-range cortico-cortical connectivity, characterized by the connectionprobability and source/target laminar patterns between any two cortical areas. For the missing data, wewill complement the experimental measurements with statistical predictions based on intrinsic relationshipsbetween the cortical structure (such as white matter distance, log ratio of neuron density and layer thick-ness) and the connectivity, for all known existing connections from the CoCoMac database [5]. Additionally,topological predictive methods will be used, when the existence of connections is unknown. The resultingmodel will be tested against electrophysiological recordings from visual, parietal and motor areas from avisuomotor experiment with macaques [6].References:[1] M. Schmidt, R. Bakker, C. C. Hilgetag, M. Diesmann, and S. J. van Albada. Multi-scale account of the network structure ofmacaque visual cortex. Brain Structure and Function, Apr. 2018.[2] M. Schmidt, R. Bakker, K. Shen, G. Bezgin, M. Diesmann, and S. J. van Albada. A multi-scale layer-resolved spikingnetwork model of resting-state dynamics in macaque visual cortical areas. PLOS Computational Biology, 14(10):e1006359,oct 2018.[3] T. C. Potjans and M. Diesmann. The cell-type specific cortical microcircuit: Relating structure and activity in a full-scalespiking network model. 24(3):785–806, Dec. 2014.[4] R. Gutzen, M. von Papen, G. Trensch, P. Quaglio, S. Grün, and M. Denker. Reproducible neural network simulations:Statistical meth-ods for model validation on the level of network activity data. Frontiers in Neuroinformatics, 12:90, 2018.[5] R. Bakker, W. Thomas, and M. Diesmann. CoCoMac 2.0 and the future of tract-tracing databases. 6:30, 2012.[6] M. J. de Haan, T. Brochier, S. Grün, A. Riehle, and F. V. Barthélemy. Real-time visuomotor behavior and electrophysiologyrecording setup for use with humans and monkeys. Journal of Neurophysiology, 120(2):539– 552, 2018. PMID: 29718806.

A problem of prime interest in neuroscience is to understand and unravel the wiring of the brain at the micro-, meso- and macroscale level and its influence on neuronal activity. While cortical network structure has been extensively studied at the level of local circuits and in terms of long-range connectivity, a spiking network model incorporating different scales, from single cells to global networks, has seldom been investigated. Here we present a multi-scale spiking network model of all vision related areas of macaque cortex [1] using the NEST simulator and outline how we aim to make use of the acquired knowledge to simulate human visual cortex.The connectivity map in our model of the macaque visual cortex integrates data on cortical architecture and axonal tracing data into a consistent multi-scale framework and predicts the connection probability between any two neurons based on their types and locations within areas and layers [1]. Each area is represented as a full-scale 1mm² microcircuit [2] with area specific architecture. Simulations using this connectivity map reveal a stable asynchronous irregular ground state with heterogeneous activity across areas, layers and populations. The network reproduces spiking statistics from electrophysiological recordings and cortico-cortical interaction patterns in fMRI functional connectivity under resting-state conditions when poised in a regime of metastable dynamics [3].The model of human visual cortex will make use of the framework developed for this macaque model, replacing neuron densities, laminar thicknesses, and cortico-cortical activity by estimates for the human brain. A first version will use published data on cortical architecture [4]. In our project, novel detailed characterizations of cortical cytoarchitecture on the level of areas and layers in the human visual cortex will be acquired. We will update our model accordingly as these data become available. Connectivity will be predicted based on these data along with inter-area distances [5]. Human-macaque homologies and DTI data will provide reference values for comparison.These models will help to elucidate how detailed connectivity of cortex shapes its dynamics on multiple scales and how prominent features of cortical activity, such as population-specific spike rates, levels of asynchrony and irregularity, resting-state functional connectivity and intrinsic time-scales, can be explained by population-level connectivity. Furthermore, this work can provide a platform for future studies of cortical functions.

Understanding and unraveling the wiring of the brain at the micro-, meso- and macroscale level and its influence on neuronal activity is a fundamental problem in neuroscience. While cortical network structure has been extensively studied at the level of local circuits and in terms of long-range connectivity, a spiking network model that integrates different scales, from single cells to global networks, has seldom been investigated. In this study we aim to simulate the vision-related areas of human cortex in a multi-area spiking network model. The model represents each area as a full-scale, 1mm² microcircuit [1] with area specific architecture and takes layer and population-resolved network connectivity into account. The procedure is based on the previously developed workflow for a multi-scale spiking network model of macaque visual cortex [2] using the NEST simulator. In this work, we will build three models based on different experimental datasets. In a first model, which simulates the whole brain, we use neuron densities and cortical thicknesses from von Economo and Koskinas [3] and DTI data from the Human Connectome Project complemented by statistical predictions of the connectivity based on cytoarchitecture[4]. In a second model we aim to use the Brainnetome parcellation and DTI data [5] and limit ourselves, as well as in the third model, to the vision-related areas of the cortex. The third model will use detailed characterization of cortical cytoarchitecture on the level of areas and layers in the human visual cortex measured by Timo Dickscheid and coworkers at INM1 and connectivity predictions by the group of Claus Hilgetag based on these data along with inter-area distances. We anticipate that reaching a plausible ground state of activity will require calibrating and stabilizing the model with mean-field theory [6].In our study, we aim to elucidate how detailed connectivity of cortex shapes its dynamics on multiple scales and how prominent features of cortical activity, such as population-specific spike rates, levels of asynchrony and irregularity, resting-state functional connectivity and intrinsic time-scales, can be explained by population-level connectivity. Furthermore, this work will provide a platform for future studies of cortical functions.

An A0 poster which is designed for fabric print and can be cut and sewed into shorts. The poster was presented at the 2017 Annual Meeting of the Organization for Computational Neuroscience (OCNS) in Antwerp, 15–20 July. Project: Brain atlases parcellate the brain into discrete regions, which form organizational units in many computational models. Many brain atlases are derived from high resolution MR images, but to get access to the cellular organization (cytoarchitecture) of the tissue, atlases based on high resolution microscopy sections are needed. Until now, the complexity and data requirements of the atlas creation process have restricted it to the confounds of specialized laboratories. Here we present an online platform to accelerate this process by allowing it to be done publicly and collaboratively, in a web-based environment. As a prototype of the platform, we created MicroDraw (http://microdraw.pasteur.fr)., This project has been funded by FLAG-ERA Joint Transnational Call (JTC) 2015.

We present a web-based neuron morphology viewer that accepts a variety of neuron morphology file formats (Neurolucida DAT/NRX/ASC/XML, SWC) and presents the resulting neuronal tree as an interactive webpage that consists of both a data-hierarchy and a 3D graphical rendering of the neuron. The viewer was created to reconcile commonly used morphology file formats into a single internal representation.

Even in the absence of a particular sensory stimulus or behavioral task, mammalian cortex features complex dynamical behavior on multiple scales. On the single-cell level, neurons spike irregularly with increasing intrinsic time scales along the visual hierarchy [3]. Cortical layers and cell types show heterogeneous average firing rates. On a global scale, cortical areas form clusters with covarying activity, so-called resting-state networks [5]. It is an open question how these multi-scale activity patterns emerge from the complex structure of cortex, with connectivity that is specific to cell types, layers, and areas.To address this question, we present a spiking multi-scale model of macaque visual cortex connected according to a new connectivity map based on multiple sources. It thus makes the nontrivial step from a population description [Chaudhuri et al.] to a spiking description of this system. Visual cortex serves as an example system on which we develop the necessary tools to simulate spiking networks of this scale with realistic population-level connectivity, enabling analogous models of further cortical regions in future.The network connectivity combines data from numerous tracing studies, collected in a new release of the CoCoMac database, with a recent quantitative dataset [6] and is refined using dynamical constraints [2]. The model for each area is an adaptation of a microcircuit model of early sensory cortex [1] to the specific laminar structure of the given area.Simulations on the JUQUEEN supercomputer show that plausible dynamics emerge from the network, mimicking experimental findings on cortical dynamics on multiple scales, from single-cell spiking properties [3] to the large-scale interaction patterns of cortical areas [5]. Furthermore, cortico-cortical interactions propagate predominantly in the feedback direction, akin to experimental observations during sleep or visual imagery [4]. Our study demonstrates that such multi-scale dynamics can emerge from the connectivity on the level of areas and populations, thereby separating network effects from the influence of diverse intrinsic dynamics of single cells.

Neural networks in visual cortex are structured into areas, layers, and neuronal populations with specific connectivity at each level. Cortical dynamics can similarly be characterized on different scales, from single-cell spiking statistics to the structured patterns of interactions between areas. A challenge of computational neuroscience is to investigate the relation of the structure of cortex to its dynamics. Network models are promising tools, but for technical and methodological reasons, they have been restricted to detailed models of one or two areas or large-scale models that reduce the internal structure of areas to a small number of differential equations.We here present a multi-scale spiking network model of all vision-related areas of macaque cortex that represents each area by a full-scale microcircuit with area-specific architecture based on a model of early sensory cortex [1]. The layer- and population-resolved network connectivity integrates axonal tracing data from the CoCoMac database with recent quantitative tracing data, and is systematically refined using dynamical constraints [2]. Gaps in the data are bridged by exploiting regularities of cortical structure such as the exponential decay of connection densities with inter-areal distance and a fit of laminar patterns versus logarithmized ratios of neuron densities. Simulations reveal a stable asynchronous irregular ground state with heterogeneous activity across areas, layers, and populations. In the presence of large-scale interactions, the model reproduces longer intrinsic time scales in higher compared to early visual areas, similar to experimental findings [3]. Activity propagates preferentially in the feedback direction, mimicking experimental results associated with visual imagery [4]. Cortico-cortical interaction patterns agree well with fMRI resting-state functional connectivity [5]. The model bridges the gap between local and large-scale accounts of cortex, and clarifies how the detailed connectivity of cortex shapes its dynamics on multiple scales.

We introduce a web-based viewer that can read, display and manipulate neuron morphology files. The files can be ingested from local storage, or by using the built-in search filters that provide access to the HBP neuroinformatics platform [1] and the extensive NeuroMorpho.org repository [2]. The viewer is the first to process common neuron morphology files in the browser, using newly written javascript-based parsers for Neurolucida data files (both binary, ascii and xml versions), as well as the SWC format [3]. Once a file is selected, the contents of the file are displayed both as a hierarchical tree and an interactive 3D graphical rendering of the neuron, powered by the x3dom.js framework. The morphology can be rendered either as a set of lines (fast) or as a collection of connected cylinders with varying diameter (slower). The viewer is available online [4,5]. An important future goal is to use the data processing services contained in the viewer in automated workflows, to support use cases that arise from the human brain project [6].