Biology subjects

Markram, H.

Publications and source records attributed to Markram, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Do we understand orientation selectivity? A simulation study

Orientation selectivity in the visual cortex has been extensively studied since it was disovered in 1959. In this article we demonstrate that current mechanistic explanations of orientation selectivity in the visual cortex, which appear to work in simplified models, do not suffice when embedded in a more realistic network. Up to now, orientation selectivity has only been reproduced in models with distance-dependent connectivity and limited cellular diversity. We therefore constructed a mouse primary visual cortex model with, among other refinements, realistic neuronal diversity and morphologically constrained connectivity. We implemented proposed mechanisms of orientation selectivity - specifically the organization of thalamocortical input into elliptical subfields and peferential connectivity between neurons with similar receptive fields - within this model. In the process we explored limitations of pure anatomy in predicting the connectivity and function of the cortex. Simulations of our model did not reproduce realistic levels of orientation selectivy. This indicates that these mechanisms, despite working well in simpler models, are not sufficient to explain orientation selectivity in real brain networks. On the basis of our results, we suggest the hypothesis that homeostatic plasticity is necessary to reconcile orientation selectivity with realistic connectivity.

neuroscience↗

bioRxiv2026

Uncovering the Basis of Human ConnectomeComplexity: The Role of Neuronal Morphology

Comparative studies have established differences between the electrophysiology and anatomy of human and rodent cortical circuits. A consistent finding is that human neuronal morphologies display more elaborate neurite shapes than those of rodents, a feature that cannot be accounted for merely by their larger size according to recent findings. Here, we study the impact of these neurite shapes on the structure of synaptic connectivity in their local microcircuitry. Our approach is based on the idea that axonal and dendritic geometries constrain the locations of afferent and efferent synaptic contacts (potential connectivity). Although the mechanisms by which potential connectivity translates into actual synaptic connectivity are manifold and complex, the potential connectivity is nevertheless highly informative for the final structure of a biological connectome. We found that connectomes predicted from human reconstructed morphologies have higher complexity according to several measures that have been demonstrated to be functionally relevant. Going beyond a simple comparison, we demonstrate mechanistically how the shapes of neuron morphologies give rise to non-random and clustered structures observed in experimentally measured connectomes, and how the specific shapes of human neurons strengthen the process. Finally, we conceptually examine how synapse formation processes may interact with potential connectivity, showing that a process compatible with Hebbian plasticity leads to the highest complexity and best match experimentally observed patterns.

neuroscience↗

bioRxiv2026

A Detailed Model for Understanding the Human Neocortex

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

neuroscience↗

bioRxiv2026

An interactive 3D atlas of gene expression in the mouse brain

The Allen Mouse Brain Atlas provides gene expression data for over 20,000 genes and has been extensively used in neuroscience. However, its constrained by a resolution of 200 {micro}m3. We improved this to 25 {micro}m3 for 4,083 genes by re-registering 401,660 sections to atlas space. From this, we produced an atlas with regions defined by shared molecular signatures and built an interactive online platform allowing exploration of gene expression patterns.

neuroscience↗

bioRxiv2024

A quantitative spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain

Brain cells can be classified into transcriptomic, morphological, and electrophysiological types. We derived a quantitative spatial atlas of the distributions of these classes within the mouse brain. To do so, we first generated a 3D atlas of transcriptomic cell type densities, by scaling regional estimates of densities from brain slices using cell counts and slice dimensions. Adjustments for regions with high cell density, such as the cerebellum, were made using the average Nissl intensities as a density modifier on a voxel-by-voxel basis. To connect the transcriptomic-type signatures to cellular function (morphological-electrophysiological types), we leveraged patch-sequencing datasets, which integrate mRNA counts, morphology reconstructions, and electrophysiological recordings from single cortical neurons. We aligned mRNA counts with the established classifications of transcriptomic types, classified neuron morphologies according to morphological types, and derived electrophysiological types using K-means clustering. The resulting 3D atlas and computational framework can be used to probe neuronal cell-type diversity and its functional implications, setting the stage for further explorations into the cellular basis of brain function.

neuroscience↗

bioRxiv2024

Higher-Order Interactions in Neuronal Function: From Genes to Ionic Currents in Biophysical Models

Neuronal firing patterns are the consequence of precise variations in neuronal membrane potential, which are themselves shaped by multiple ionic currents. In this study, we use biophysical models, statistical methods, and information theory to explore the interaction between these ionic currents and neuron electrophysiological phenotype. We created numerous electrical models with diverse firing patterns. By analyzing these models, we identified intricate relationships between model parameters and electrical features. Our findings show that neuronal activity is often influenced by multiple biophysical model parameters, in a non-additive (i.e. synergistic) fashion. When comparing this with single-cell RNAseq data, we found a contrasting structure: gene expression profiles were dominated by redundancy, reflecting differences in regulatory constraints and sampling diversity. This research sheds light on the complex links between biophysical parameters and neuronal phenotypes.

neuroscience↗

bioRxiv2024

A multiscale electro-metabolic model of a rat neocortical circuit reveals the impact of ageing on central cortical layers

The high energetic demands of the brain arise primarily from neuronal activity. Neurons consume substantial energy to transmit information as electrical signals and maintain their resting membrane potential. These energetic requirements are met by the neuro-glial-vascular (NGV) ensemble, which generates energy in a coupled metabolic process. In ageing, metabolic function becomes impaired, producing less energy and, consequently, the system is unable to sustain the neuronal energetic needs. We propose a multiscale model of electro-metabolic coupling in a reconstructed rat neocortex. This combines an electro-morphologically reconstructed electrophysiological model with a detailed NGV metabolic model. Our results demonstrate that the large-scale model effectively captures electro-metabolic processes at the circuit level, highlighting the importance of heterogeneity within the circuit, where energetic demands vary according to neuronal characteristics. Finally, in metabolic ageing, our model indicates that the middle cortical layers are particularly vulnerable to energy impairment.

neuroscience↗

bioRxiv2024

Modeling of blood flow dynamics in the rat somatosensory cortex

The cerebral microvasculature forms a dense network of interconnected blood vessels where flow is modulated partly by astrocytes. Increased neuronal activity stimulates astrocytes to release vasoactive substances at the endfeet, altering the diameters of connected vessels. Our study simulated the coupling between blood flow variations and vessel diameter changes driven by astrocytic activity in the rat somatosensory cortex. We developed a framework with three key components: coupling between vasculature and synthesized astrocytic morphologies, a fluid dynamics model to compute flow in each vascular segment, and a stochastic process replicating the effect of astrocytic endfeet on vessel radii. The model was validated against experimental flow values from literature across cortical depths. We found that local vasodilation from astrocyte activity increased blood flow, especially in capillaries, exhibiting a layer-specific response in deeper cortical layers. Additionally, the highest blood flow variability occurred in capillaries, emphasizing their role in cerebral perfusion regulation. We discovered that astrocytic activity impacts blood flow dynamics in a localized, clustered manner, with most vascular segments influenced by two to three neighboring endfeet. These insights enhance our understanding of neurovascular coupling and guide future research on blood flow-related diseases.

neuroscience↗

bioRxiv2024

An extended and improved CCFv3 annotation and Nissl atlas of the entire mouse brain

Brain atlases are essential for quantifying cellular composition in mouse brain regions. The Allen Institutes Common Coordinate Framework version 3 (CCFv3) is widely used, delineating over 600 anatomical regions, but it lacks coverage for the most rostral and caudal brain parts, including the main olfactory bulb, cerebellum, and medulla. Additionally, the CCFv3 omits key cerebellar layers, and its corresponding Nissl-stained reference volume is not precisely aligned, limiting its utilisability. To address these issues, we developed an extended atlas, the Blue Brain Project CCFv3 augmented (CCFv3aBBP), which includes a fully annotated mouse brain and an improved Nissl reference aligned in the CCFv3. This enhanced atlas also features the central nervous system annotation (CCFv3cBBP). Using this resource, we aligned 734 Nissl-stained brains to produce an average Nissl template, enabling an updated distribution of neuronal soma positions. These data are available as an open-source resource, broadening applications such as improved alignment precision, cell type mapping, and multimodal data integration.

bioinformatics↗

bioRxiv2024

Computational generation of long-range axonal morphologies

Long-range axons are fundamental to brain connectivity and functional organization, enabling communication between different regions of the brain. Recent advances in experimental techniques have yielded a substantial number of whole-brain axonal reconstructions. While most previous computational generative models of neurons have predominantly focused on dendrites, generating realistic axonal morphologies is challenging due to their distinct targeting. In this study, we present a novel algorithm for axon synthesis that combines algebraic topology with the Steiner tree algorithm, an extension of the minimum spanning tree, to generate both the local and long-range compartments of axons. We demonstrate that our computationally generated axons closely replicate experimental data in terms of their morphological properties. This approach enables the generation of biologically accurate long-range axons that span large distances and connect multiple brain regions, advancing the digital reconstruction of the brain. Ultimately, our approach opens up new possibilities for large-scale in-silico simulations, advancing research into brain function and disorders.

neuroscience↗

bioRxiv2024

Generating brain-wide connectome using synthetic axonal morphologies

Recent experimental advancements, including electron microscopy reconstructions, have produced detailed connectivity data for local brain regions. On the other hand, for inter-regional connectivity, large-scale imaging techniques such as MRI are best suited to provide insights. However, understanding the relationship between local and long-range connectivity is essential for studying both healthy and pathological conditions of the brain. Leveraging a novel dataset of whole-brain axonal reconstructions, we present a technique to predict whole-brain connectivity at single cell level by generating detailed whole-brain axonal morphologies from sparse experimental data. The computationally generated axons accurately reproduce the local and global morphological properties of experimental reconstructions. Furthermore, the computationally synthesized axons generate large-scale inter-regional connectivity, defining the projectome and the connectome of the brain, thereby enabling the in silico experimentation of large brain regions.

neuroscience↗

bioRxiv2024

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Neuronal shape determines how neurons process and integrate information, yet a consistent and objective classification of neuronal morphologies remains elusive. Current approaches rely heavily on subjective expert views or on predefined features, limiting reproducibility and interpretability. Here, we present an interpretable deep learning framework that unifies topological data analysis, graph neural networks, and traditional morphometrics to classify neuronal morphologies objectively and transparently. Our framework compares complementary mathematical representations of neurons to capture geometric, topological, and graph-structural information. Then it benchmarks their performance against expert-labeled datasets. We show that topology- and graph-based models achieve accuracies comparable to human experts, revealing that both global branching invariants and local connectivity patterns are essential to define morphological cell types. Using explainable artificial intelligence methods, we identify structural features driving each classification decision, bridging computational and neuroanatomical interpretations. This open source and reproducible approach provides a foundation for scalable, interpretable and biologically meaningful neuronal taxonomy, enabling consistent comparisons between data sets and species.

neuroscience↗

bioRxiv2023

Of mice and men: Increased dendritic complexity gives rise to unique human networks

The organizational principles that distinguish the human brain from other species have been a long-standing enigma in neuroscience. Focusing on the uniquely evolved human cortical layers 2 and 3, we computationally reconstruct the cortical architecture for mice and humans. We show that human pyramidal cells form highly complex networks, demonstrated by the increased number and simplex dimension compared to mice. This is surprising because human pyramidal cells are much sparser in the cortex. We show that the number and size of neurons fail to account for this increased network complexity, suggesting that another morphological property is a key determinant of network connectivity. Topological comparison of dendritic structure reveals much higher perisomatic (basal and oblique) branching density in human pyramidal cells. Using topological tools we quantitatively show that this neuronal structural property directly impacts network complexity, including the formation of a rich subnetwork structure. We conclude that greater dendritic complexity, a defining attribute of human L2 and 3 neurons, may provide the human cortex with enhanced computational capacity and cognitive flexibility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/557170v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@c0adadorg.highwire.dtl.DTLVardef@1ca4aaeorg.highwire.dtl.DTLVardef@1ecf1b5org.highwire.dtl.DTLVardef@f0d44d_HPS_FORMAT_FIGEXP M_FIG C_FIG A. A multiscale analysis was performed to compare the mouse and human brains: from the anatomical properties of brain regions to the morphological details of single neurons. B. Human circuits are larger than mice in terms of size and number of neurons, but present decreased neuron density, resulting in increased distances between neurons, particularly among pyramidal cells. C. Greater network complexity emerges within the human brain. Network complexity is defined by larger groups of neurons forming complex interconnections throughout the network. D. The topological analysis of layer 2/3 pyramidal cells in the temporal cortex reveals an intriguing difference: human neurons exhibit a significantly larger number of dendritic branches, especially near the cell body compared to mice. This phenomenon is termed "higher topological complexity" in dendrites. Our findings suggest that dendritic complexity wields a more substantial influence on network complexity than neuron density.

neuroscience↗

bioRxiv2023

Breakdown and rejuvenation of aging brain energy metabolism

The study presented explores the complex relationship between the aging brain, energy metabolism, blood flow and neuronal activity by introducing a comprehensive, data-driven molecular model of the neuro-glial vascular system, including all key enzymes, transporters, metabolites, and blood flow vital for neuronal electrical activity with 16800 interaction pathways. We find significant alterations in metabolite concentrations and differential effects on ATP supply in neurons and astrocytes and within subcellular compartments within aged brains, and identify reduced Na+/K+-ATPase as the leading cause of impaired neuronal action potentials. The model predicts that the metabolic pathways cluster more closely in the aged brain, suggesting a loss of robustness and adaptability. Additionally, the aged metabolic system displays reduced flexibility, undermining its capacity to efficiently respond to stimuli and recover from damage. Through transcription factor analysis, the estrogen-related receptor alpha (ESRRA) emerged as a central target connected to these aging-related changes. An unguided optimization search pinpointed potential interventions capable of restoring the brains metabolic flexibility and restoring action potential generation. These strategies include increasing the NADH cytosol-mitochondria shuttle, NAD+ pool, ketone {beta}-hydroxybutyrate, lactate and Na+/K+-ATPase and reducing blood glucose levels. The model is open-sourced to help guide further research in brain metabolism.

neuroscience↗

bioRxiv2023

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a model comprising eight somatosensory cortex subregions, 4.2 million morphological and electrically-detailed neurons, and 13.2 billion local and mid-range synapses. In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. The model reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas. The models direct correspondence with biology generated predictions about how multiscale organization shapes activity; for example, how cortical activity is shaped by high-dimensional connectivity motifs in local and mid-range connectivity, and spatial targeting rules by inhibitory subpopulations. The latter was facilitated using a rewired connectome which included specific targeting rules observed for different inhibitory neuron types in electron microscopy. The model also predicted the role of inhibitory interneuron types and different layers in stimulus encoding. Simulation tools and a large subvolume of the model are made available to enable further community-driven improvement, validation and investigation.

neuroscience↗

bioRxiv2023

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

Variability is a universal feature among biological units such as neuronal cells as they enable a robust encoding of a high volume of information in neuronal circuits and prevent hyper synchronizations such as epileptic seizures. While most computational studies on electrophysiological variability in neuronal circuits were done with simplified neuron models, we instead focus on the variability of detailed biophysical models of neurons. With measures of experimental variability, we leverage a Markov chain Monte Carlo method to generate populations of electrical models able to reproduce the variability from sets of experimental recordings. By matching input resistances of soma and axon initial segments with the one of dendrites, we produce a compatible set of morphologies and electrical models that faithfully represent a given morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells with continuous adapting firing type and show that morphological variability is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.

neuroscience↗

bioRxiv2022

A universal workflow for creation, validation and generalization of detailed neuronal models

Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex (SSCx). Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms specific to the cell type. We use an evolutionary algorithm to optimize passive and active ionic parameters to match the electrophysiological features extracted from whole-cell patch-clamp recordings. To shed light on which parameters are constrained by experimental data and which could be degenerate, we perform a parameter sensitivity analysis. We also validate the optimized models against additional experimental stimuli and assess their generalizability on a population of morphologies with the same morphological type. With this workflow, we generate SSCx neuronal models producing the variability of neuronal responses. Due to its versatility, our workflow can be used to build robust biophysical models of any neuronal type.

neuroscience↗

bioRxiv2022

Neuromodulatory organization in the developing rat somatosensory cortex

The vast majority of cortical synapses are found in the neuropil which is implicated in multiple and diverse functions underlying brain computation. Unraveling the organizing principles of the cortical neuropil requires an intricate characterization of synaptic connections established by excitatory and inhibitory axon terminals, of intrinsic and extrinsic origin and from ascending projections that govern the function of cortical microcircuits through the release of neuromodulators either through point-to-point chemical synapses or diffuse volume transmission (VT). Even though neuromodulatory release has been studied for almost a century it is still not clear if one modality prevails upon the other. The hindlimb representation of the somatosensory cortex (HLS1) of two-week old Wistar rats has served as a model system to dissect the microcircuitry of neurons and their synaptic connections. In the present study, we quantified the fiber length per cortical volume and the density of varicosities for cholinergic, catecholaminergic and serotonergic neuromodulatory systems in the cortical neuropil using immunocytochemical staining and stereological techniques. Acquired data were integrated into a novel computational framework to reconcile the specific modalities and predict the effects of neuromodulatory release in shaping neocortical network activity. We predict that acetylcholine (ACh), dopamine (DA), serotonin (5-HT) release desynchronizes cortical activity by inhibiting slow oscillations (delta range), and that 5-HT triggers faster oscillations (theta). Moreover, we found that high levels (>40%) of neuromodulatory VT are sufficient to induce network desynchronization, but also that combining volume release with synaptic inputs leads to more robust and stable effects, meaning that lower levels of VT are needed to achieve the same outcome (10%).

neuroscience↗