Biology subjects

Markram, H.

Publications and source records attributed to Markram, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.

neuroscience↗

bioRxiv2022

A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of non-somatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density micro-electrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at sub-cellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multi-modal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and to provide the field with neuronal models that are more realistic and can be better validated. Author SummaryMulticompartment models are one of the most biophysically detailed representations of single neurons. The vast majority of these models are built using experimental data from somatic recordings. However, neurons are much more than just their soma and one needs recordings from distal neurites to build an accurate model. In this article, we combine the patch-clamp technique with extracellular high-density microelectrode arrays (HD-MEAs) to compensate this shortcoming. In fact, HD-MEAs readouts allow one to record the neuronal signal in the entire axonal arbor. We show that the proposed multi-modal strategy is superior to the use of patch clamp alone using an existing model as ground-truth. Finally, we show an application of this strategy on experimental data from cultured neurons.

neuroscience↗

bioRxiv2022

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience

UO_SCPLOWLTRALISERC_SCPLOW is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. UO_SCPLOWLTRALISERC_SCPLOW implements a high performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of UO_SCPLOWLTRALISERC_SCPLOW is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. UO_SCPLOWLTRALISERC_SCPLOW is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SignificanceThere is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present UO_SCPLOWLTRALISERC_SCPLOW, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations. Key pointsO_LIUltraliser creates spatial models of neuro-glia-vascular (NGV) structures with realistic geometries. C_LIO_LIUltraliser creates high fidelity watertight manifolds and large scale volumes from centerline descriptions, non-watertight surfaces, and binary masks. C_LIO_LIResulting models enable scalable in silico experiments that can probe intricate structure-function relationships. C_LIO_LIThe framework is unrivalled both in ease-of-use and in the accuracy of resulting geometry representing a major leap forward in simulation-based neuroscience. C_LI

bioinformatics↗

bioRxiv2022

Reconstruction and simulation of thalamoreticular microcircuitry

Thalamoreticular circuitry is known to play a key role in attention, cognition and the generation of sleep spindles, and is implicated in numerous brain disorders, but the cellular and synaptic mechanisms remain intractable. Therefore, we developed the first detailed computational model of mouse thalamus and thalamic reticular nucleus microcircuitry that captures morphological and biophysical properties of [~]14,000 neurons connected via [~]6M synapses, and recreates biological synaptic and gap junction connectivity. Simulations recapitulate multiple independent network-level experimental findings across different brain states, providing a novel unifying cellular and synaptic account of spontaneous and evoked activity in both wakefulness and sleep. Furthermore, we found that: 1.) inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness, in addition to its role in spindle generation; 2.) thalamic interactions generate the characteristic waxing and waning of spindle oscillations; and 3.) changes in thalamic excitability (e.g. due to neuromodulation) control spindle frequency and occurrence. The model is openly available and provides a new tool to interpret spindle oscillations and test hypotheses of thalamoreticular circuit function and dysfunction across different network states in health and disease.

neuroscience↗

bioRxiv2022

Using the glial mitochondrial machinery to prevent multilevel dysfunctions observed in a mouse model of Alzheimer s disease

Alzheimers disease (AD) is becoming increasingly prevalent worldwide. It represents one of the greatest medical challenge as no pharmacologic treatments are available to prevent disease progression. Astrocytes play crucial functions within neuronal circuits by providing metabolic and functional support, regulating interstitial solute composition, and modulating synaptic transmission. In addition to these physiological functions, growing evidence points to an essential role of astrocytes in neurodegenerative diseases like AD. Early stage AD is associated with hypometabolism and oxidative stress. Contrary to neurons that are vulnerable to oxydative stress, astrocytes are particularly resistant to mitochondrial dysfunction and are therefore more resilient cells. In our study, we leveraged astrocytic mitochondrial uncoupling and examined neuronal function in the 3xTg AD mouse model. We overexpressed the mitochondrial uncoupling protein 4 (UCP4), which has been shown to improve neuronal survival in vitro. We found that this treatment efficiently prevented alterations of hippocampal metabolite levels observed in AD mice, along with hippocampal atrophy and reduction of basal dendrite arborization of subicular neurons. This approach also averted aberrant neuronal excitability observed in AD subicular neurons and preserved episodic-like memory in AD mice assessed in a spatial recognition task. These findings show that targeting astrocytes and their mitochondria is an effective strategy to prevent the decline of neurons facing AD-related stress at the early stages of the disease.

neuroscience↗

bioRxiv2021

Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons

Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterised morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 13 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex. Author SummaryThe computational abilities of the brain arise from its organisation principles at the cellular level. One of these principles is the neuronal type composition over different regions. Since computational functions of neurons are best described by their morphological and electrophysiological properties, it is logical to use morpho-electrically defined cell types to describe brain composition. However, characterizing morpho-electrical properties of cells involve low-throughput techniques not very well suited to scan the whole brain. Thanks to recent progress on transcriptomic and immuno-staining techniques we are now able to get a more accurate snapshot of the mouse brain composition for molecularly defined cell types.How to link molecularly defined cell types with morpho-electrical cell types remains an open question. Several studies have explored this problem providing valuable three-modal datasets combining electrical, morphological and molecular properties of cortical neurons. The long-term goal of the Blue Brain Project (BBP) is to accurately model the mouses whole brain, which requires detailed biophysical models of neurons. Instead of going through the time-consuming process of producing detailed models from the three-modal datasets, we explored a time-saving method. We mapped the already available detailed morpho-electrical models from the BBP rat dataset to cells from a three-modal mouse dataset. We thus assigned a molecular identity to the neuron models allowing us to populate the whole mouse cortex with detailed neuron models.

neuroscience↗

bioRxiv2021

Atlas of Inhibitory Neurons in the Mouse Brain

The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region. Author summaryObtaining a global understanding of the cellular composition of the brain is a very complex task, not only because of the great variability that exists between reports of similar counts but also because of the numerous brain regions and cell types that make up the brain. Previously, we presented a model of a cell atlas, which provided an estimate of the densities of neurons, glia and their subtypes for each region in the mouse brain. Here, we describe an extension of this model to include more inhibitory neuron types. We collected estimates of inhibitory neuron counts from literature and built a framework to combine them into a consistent cell atlas. Using brain slice images, we also estimated inhibitory neuron density in regions where no literature data are available. We estimated that in the mouse brain 20.3% of all neurons are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. Our approach can be further extended to any other cell type and provides a resource to build tissue-level models of the rodent brain.

neuroscience↗

bioRxiv2021

The role of hub neurons in modulating cortical dynamics

Many neurodegenerative diseases are associated with the death of specific neuron types in particular brain regions. What makes the death of specific neuron types particularly harmful for the integrity and dynamics of the respective network is not well understood. To start addressing this question we used the most up-to-date biologically-realistic dense neocortical microcircuit (NMC) of rodent, which has reconstructed a volume of 0.3 mm3 and containing 31,000 neurons, 36 million synapses, and 55 morphological cell types arranged in 6 cortical layers. Using modern network science tools, we identified "hub-neurons" in the NMC, that are connected synaptically to a large number of their neighbors and systematically examined the impact of abolishing these cells. In general, the structural integrity of the network is robust to cells attack; yet, attacking hub neurons strongly impacted the "small worldness" topology of the network, whereas similar attacks on random neurons have a negligible effect. Such hub-specific attacks are also impactful on the network dynamics, both when the network is at its spontaneous synchronous state and when it was presented with synchronized thalamo-cortical visual-like input. We found that attacking layer 5 hub neurons are most harmful to the structural and functional integrity of the NMC. The significance of our results for understanding the role of specific neuron types and cortical layers for disease manifestation is discussed.

neuroscience↗

bioRxiv2021

Architecture of the Neuro-Glia-Vascular System

Astrocytes connect the vasculature to neurons and mediate the supply of nutrients and biochemicals. They also remove metabolites from the neurons and extracellular environment. They are involved in a growing number of physiological and pathophysiological processes. Understanding the biophysical, physiological, and molecular interactions in this neuro-glia-vascular ensemble (NGV) and how they support brain function is severely restricted by the lack of detailed cytoarchitecture. To address this problem, we used data from multiple sources to create a data-driven digital reconstruction of the NGV at micrometer anatomical resolution. We reconstructed 0.2 mm3 of rat somatosensory cortical tissue with approximately 16000 morphologically detailed neurons, its microvasculature, and approximately 2500 morphologically detailed protoplasmic astrocytes. The consistency of the reconstruction with a wide array of experimental measurements allows novel predictions of the numbers and locations of astrocytes and astrocytic processes that support different types of neurons. This allows anatomical reconstruction of the spatial microdomains of astrocytes and their overlapping regions. The number and locations of end-feet connecting each astrocyte to the vasculature can be determined as well as the extent to which they cover the microvasculature. The structural analysis of the NGV circuit showed that astrocytic shape and numbers are constrained by vasculatures spatial occupancy and their functional role to form NGV connections. The digital reconstruction of the NGV is a resource that will enable a better understanding of the anatomical principles and geometric constraints which govern how astrocytes support brain function. Table of contentsO_ST_ABSMain pointsC_ST_ABSO_LIThe Blue Brain Project digitally reconstructs a part of neocortical Neuro-Glia-Vascular organization C_LIO_LIInterdependencies and topological methods allow dense in silico reconstruction from sparse experimental data C_LIO_LIThe polarized role of protoplasmic astrocytes constrains their shapes and numbers C_LI Table of contents image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/427241v3_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1260843org.highwire.dtl.DTLVardef@1e88718org.highwire.dtl.DTLVardef@fd66c5org.highwire.dtl.DTLVardef@6628a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response

The nervous system is notorious for its strong response evoked by a surprising sensory input, but the biophysical and anatomical underpinnings of this phenomenon are only partially understood. Here we utilized in-silico experiments of a biologically-detailed model of a neocortical microcircuit to study stimulus specific adaptation (SSA) in the auditory cortex, whereby the neuronal response adapts significantly for a repeated ("expected") tone but not for a rare ("surprise") tone. SSA experiments were mimicked by stimulating tonotopically-mapped thalamo-cortical afferents projecting to the microcircuit; the activity of these afferents was modeled based on our in-vivo recordings from individual thalamic neurons. The modeled microcircuit expressed naturally many experimentally-observed properties of SSA, suggesting that SSA is a general property of neocortical microcircuits. By systematically modulating circuit parameters, we found that key features of SSA depended on synergistic effects of synaptic depression, spike frequency adaptation and recurrent network connectivity. The relative contribution of each of these mechanisms in shaping SSA was explored, additional SSA-related experimental results were explained and new experiments for further studying SSA were suggested.

neuroscience↗

bioRxiv2020

A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

Long-term potentiation (LTP) and long-term depression (LTD) of pyramidal cell connections are among the key mechanisms underlying learning and memory in the brain. Despite their important role, only a few of these connections have been characterized in terms of LTP/LTD dynamics, such as the one between layer 5 thick-tufted pyramidal cells (L5-TTPCs). Comparing the available evidence on different pyramidal connection types reveals a large variability of experimental outcomes, possibly indicating the presence of connection-type-specific mechanisms. Here, we show that a calcium-based plasticity rule regulating L5-TTPC synapses holds also for several other pyramidal-to-pyramidal connections in a digital model of neocortical tissue. In particular, we show that synaptic physiology, cell morphology and innervation patterns jointly determine LTP/LTD dynamics without requiring a different model or parameter set for each connection type. We therefore propose that a similar set of plasticity mechanisms is shared by seemingly very different neocortical connections and that only a small number of targeted experiments is required for generating a complete map of synaptic plasticity dynamics in the neocortex.

neuroscience↗

bioRxiv2020

Computational synthesis of cortical dendritic morphologies

Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles and investigating structural alterations associated with diseased brain states. Recently, we introduced a topological descriptor that reliably categorizes dendritic morphologies. We apply this descriptor to digitally synthesize dendrites to address the challenge of insufficient biological reconstructions. The synthesized cortical dendrites are statistically indistinguishable from the corresponding reconstructed dendrites in terms of morpho-electrical properties and connectivity. This topology-guided synthesis enables the rapid digital reconstruction of entire brain regions from relatively few reference cells, thereby allowing the investigation of links between neuronal morphologies and brain function across different spatio-temporal scales. We synthesized cortical networks based on structural alterations of dendrites associated with medical conditions and revealed principles linking branching properties to the structure of large-scale networks. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/040410v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@1ae790org.highwire.dtl.DTLVardef@58c3eborg.highwire.dtl.DTLVardef@1a6a9b1org.highwire.dtl.DTLVardef@ceae91_HPS_FORMAT_FIGEXP M_FIG Graphical abstractA topological model of neuronal shapes is used to investigate the link between the branching patterns of dendritic morphologies and the connectivity of the neuronal networks they form. Starting from reconstructed cells (in black) of cortical dendrites, we extract the topological barcode that is used to create a statistically similar synthesized pyramidal cell (in red), and respectively a group of pyramidal cells of the same morphological type. From reconstructed cells examples of all layers and morphological types we generate synthesized dendrites and build a synthesized cortical column (colors corresponds to cortical layers). The synthesized dedrites are statistically similar to the reconstructed dendrites in terms of morpho-electrical properties and the connectome of the synthesized column (colored connectome) is almost indistinguishable from the connectome of the reconstructed column (greyscale). C_FIG

neuroscience↗

bioRxiv2019

Voltage-sensitive dye imaging reveals inhibitory modulation of ongoing cortical activity

Voltage-sensitive dye imaging (VSDI) is a powerful technique for interrogating membrane potential dynamics in assemblies of cortical neurons, but with effective resolution limits that confound interpretation. In particular, it is unclear how VSDI signals relate to population firing rates. To address this limitation, we developed an in silico model of VSDI in a biologically faithful digital reconstruction of rodent neocortical microcircuitry. Using this model, we extend previous experimental observations regarding the cellular origins of VSDI, finding that the signal is driven primarily by neurons in layers 2/3 and 5. We proceed by exploring experimentally inaccessible circuit properties to show that during periods of spontaneous activity, membrane potential fluctuations are anticorrelated with population firing rates. Furthermore, we manipulate network connections to show that this effect depends on recurrent connectivity and is modulated by external input. We conclude that VSDI primarily reflects inhibitory responses to ongoing excitatory dynamics.

neuroscience↗

bioRxiv2019

Impact of higher-order network structure on emergent cortical activity

Synaptic connectivity between neocortical neurons is highly structured. The network structure of synaptic connectivity includes first-order properties that can be described by pairwise statistics, such as strengths of connections between different neuron types and distance-dependent connectivity, and higher-order properties, such as an abundance of cliques of all-to-all connected neurons. The relative impact of first- and higher-order structure on emergent cortical network activity is unknown. Here, we compare network structure and emergent activity in two neocortical microcircuit models with different synaptic connectivity. Both models have a similar first-order structure, but only one model includes higher-order structure arising from morphological diversity within neuronal types. We find that such morphological diversity leads to more heterogeneous degree distributions, increases the number of cliques, and contributes to a small-world topology. The increase in higher-order network structure is accompanied by more nuanced changes in neuronal firing patterns, such as an increased dependence of pairwise correlations on the positions of neurons in cliques. Our study shows that circuit models with very similar first-order structure of synaptic connectivity can have a drastically different higher-order network structure, and suggests that the higher-order structure imposed by morphological diversity within neuronal types has an impact on emergent cortical activity.

neuroscience↗

bioRxiv2019

Data-driven integration of hippocampal CA1 synapse physiology in silico

The anatomy and physiology of synaptic connections in rodent hippocampal CA1 have been exhaustively characterized in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data-driven approach to integrate the current state-of-the-art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo-dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short-term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired-recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different labs, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource towards a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.

neuroscience↗

bioRxiv2019

Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks

Why do cortical microcircuits in a variety of brain regions express similar, highly nonrandom, network motifs? To what extent this structure is innate and how much of it is molded by plasticity and learning processes? To address these questions, we developed a general network science framework to quantify the contribution of neurons geometry and their embedding in cortical volume to the emergence of three-neuron network motifs. Applying this framework to a dense in silico reconstructed cortical microcircuits showed that the innate asymmetric neurons geometry underlies the universally recurring motif architecture. It also predicted the spatial alignment of cells composing the different triplets-motifs. These predictions were directly validated via in vitro 12-patch whole-cell recordings (7,309 triplets) from rat somatosensory cortex. We conclude that the local geometry of neurons imposes an innate, already structured, global network architecture, which serves as a skeleton upon which fine-grained structural and functional plasticity processes take place.

neuroscience↗

bioRxiv2019

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

Previous studies based on the Quantal Model for synaptic transmission suggested that neurotransmitter release is mediated by a single release site at individual synaptic contacts in the neocortex. However, recent studies seem to contradict this hypothesis and indicate that multi-vesicular release (MVR) could better explain the synaptic response variability observed in vitro. In this study we present a novel method to estimate the number of release sites per synapse, also known as the size of the readily-releasable pool (NRRP), from paired whole-cell recordings of layer 5 thick tufted pyramidal cell (L5_TTPC) connections in the somatosensory neocortex. Our approach extends the work of Loebel and colleagues to take advantage of a recently reported data-driven biophysical model of neocortical tissue. Using this approach, we estimated NRRP to be between two to three for connections between L5-TTPC. To constrain NRRP values for other connections in the microcircuit, we developed and validated a generalization approach using data on post-synaptic potential (PSP) coefficient of variations (CVs) from literature and matching to in silico experiments. Our study shows that synaptic connections in the neocortex generally are mediated by MVR and provides a data-driven approach to constrain the MVR model parameters of the microcircuit.

neuroscience↗

bioRxiv2019

A null model of the mouse whole-neocortex micro-connectome

1Connectomics, the study of the structure of networks of synaptically connected neurons, is one of the most important frontiers of neuroscience. Great advances are being made on the level of macro- and meso-scale connectomics, that is the study of how and which populations of neurons are wired together by tracing axons of anatomically and genetically defined neurons throughout the brain. Similarly, the use of electron-microscopy and statistical connectome models has improved our understanding of micro-connectomics, that is the study of connectivity patterns between individual neurons. We have combined these two complementary views of connectomics to build a first draft statistical model of the neuron-to-neuron micro-connectome of a whole mouse neocortex. We combined available data on region-to-region connectivity and individual whole-brain axon reconstructions to model in addition to the meso-scale trends also the innervation of individual neurons by individual axons, within and across regions. This process revealed a novel targeting principle that allowed us to predict the innervation logic of individual axons from meso-scale data. The resulting micro-connectome of 10 million neurons and 88 billion synapses recreates biological trends of targeting on the macro-meso- and micro-scale, i.e. targeting of brain regions, domains and layers within a brain region down to individual neurons. This openly accessible connectome can serve as a powerful null model to compare experimental findings to and as a substrate for whole-brain simulations of detailed neural networks.

neuroscience↗