Biology subjects

Reimann, M. W.

Publications and source records attributed to Reimann, M. W..

At least 19 recordsLinked to original sources

bioRxiv2026

Connectivity Logic of Dendritic Spines in Cortex: Increased Inputs and Ensemble Formation

Dendritic spines are the principal postsynaptic targets of excitatory synapses, yet the network logic governing their organization and contribution to cortical computation remains unclear. Here, we combine the MICrONS ultrastructural reconstruction of mouse visual cortex with matched in vivo two-photon calcium imaging to explore the network roles of dendritic spines. Structurally, we show that spine density predicts the number and diversity of both excitatory and inhibitory presynaptic partners, supporting the long-standing "connectivity and diversity" hypothesis. Beyond expanding input space, spines exhibit distinct network-level principles: excitatory axons preferentially target spines with increasing axonal distance from the soma; highly broadcasting ("hub") neurons preferentially innervate spines; and neurons sharing presynaptic partners preferentially route their own outputs onto spines, revealing an input-to-spine/output-to-spine wiring correlations. Functionally, we find that these same structural motifs are found in neuronal ensembles, defined as groups of neurons with correlated calcium activity. Ensemble members are interconnected at more than twice the rate of spatially matched controls; these synapses connecting them are routed almost exclusively onto dendritic spines; and their shared presynaptic partners are almost exclusively inhibitory neurons that themselves also preferentially target spines. We suggest that local recurrent spine-targeted excitation effectively binds ensemble members into a coactive subnetwork, while shared spine-directed inhibition gates, synchronizes, and stabilizes their collective activity, providing a circuit-level architecture that may support attractor dynamics, pattern completion, and other cortical computations.

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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.

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bioRxiv2025

Neuron morphological physicality and variability define the non-random structure of connectivity

Neuronal networks are characterized by complex and functionally relevant connectivity motifs. We developed an intuitive explanation for its emergence: While a class of neurons on average innervates its entire surroundings, each individual one can only cover a small part of the space. That region is different for each neuron, but not completely random, as it is physically constrained by the spatial continuity of the axon. This hypothesis was successfully tested against a morphologically-detailed model and an electron-microscopic reconstruction of cortical connectivity. We distilled it into a stochastic algorithm that generates networks, which accurately match the reference data. Our work bridges previous efforts to capture network complexity with top-down or bottom-up methods, that is, by adding complexity constraints to simple stochastic models or by predicting synapses from neuron appositions. It may improve the understanding of the impact of neuron malformations and the functional role of non-random network structure in simplified models.

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bioRxiv2025

An algorithm to model the non-random connectome of cortical circuitry

Neuronal connectivity has been characterized at various scales and with respect to various structural aspects. In models of connectivity, it has so far remained difficult to match all of them at once, in particular the higher-order structure appears to be elusive. Here we introduce a new type of graph model that matches non-random structure characterized and described as relevant in biological neuronal networks. The structure emerges because the algorithm considers the need for axons to physically bridge the gap from soma to dendrites. If it targets one neuron, probabilities that it also targets other nearby neurons increase. We demonstrate that the algorithm can be successfully fit to complex, biologically relevant reference connectomes. Furthermore, we outline an intuitive expansion of the model from merely local to a combination of local and long-range connectivity. We provide a performant implementation that can be used to instantiate point neuron or morphologically-detailed network models at whole-cortex scale.

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bioRxiv2024

Computational modeling reveals biological mechanisms underlying the whisker-flick EEG

Whisker flick stimulation is a commonly used protocol to investigate somatosensory processing in rodents. Neural activity in the brain evoked by whisker flicks produces a characteristic EEG waveform recorded at the skull, known as a somatosensory evoked potential. In this paper, we use in silico modeling to identify the neural populations that serve as sources and targets of the synaptic currents contributing to this signal (presynaptic and postsynaptic populations, respectively). The initial positive deflection of the EEG waveform is driven largely by direct thalamic inputs to Layer 2/3 and Layer 5 pyramidal cells, though interestingly, L5-L5 inhibition plays a modulatory role, reducing the amplitude and width of the deflection. This suggests that increasing thalamocortical connectivity and decreasing L5-L5 inhibition may be responsible for some of the changes observed in the EEG waveform over the course of development. The negative deflection is driven by a more complex mix of sources, including both thalamic and recurrent cortical connectivity. We demonstrate that small changes to the local connectivity of the circuit, particularly to perisomatic inhibitory targeting, can have an important impact on the recorded EEG, without substantially affecting firing rates, suggesting that EEG may be useful in constraining in silico neural models.

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bioRxiv2024

Spike sorting biases and information loss in a detailed cortical model

Sorting action potentials (spikes) from extracellular recordings of large groups of connected neurons is essential to understanding brain function. Simulations with known spike times have driven significant advances in spike sorting, but present models do not account for neuronal heterogeneity and its effect on sorting accuracy. Here, we used a large-scale detailed cortical microcircuit model to simulate recordings, evaluate modern spike sorters, and link their performance to neuronal heterogeneity. We also exposed the network to various stimuli to investigate how sorting errors affect stimulus discrimination. Spike sorters successfully isolated about 10% of neurons within 50 {micro}m of the electrode shank. This undersampling had no impact on stimulus discrimination ability. However, sorting biases related to firing rate, spike extent, synaptic type, and layer reduced its discrimination ability by nearly half. These findings show realistic models are a complementary method to evaluate and improve spike sorting and, hence, improve our understanding of neural activity.

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bioRxiv2024

Simulation Insights on the Compound Action Potential in Multifascicular Nerves

ObjectiveDevelop an efficient method for simulating evoked compound action potential (eCAP) signals from complex nerves to help optimize and interpret eCAP recordings; validate it through comparison with measured vagus nerve eCAP recordings; elucidate the subtle interplay giving rise to specific eCAP signal shapes and magnitudes. ApproachWe developed an extended reciprocity theorem approach to model neuron signals in heterogeneous environments, and use it to study analytically the single fibre action potential. We then established a semi-analytic model that also uses hybrid electromagnetic-electrophysiological simulations to model eCAP signals from complex nerves populated with heterogeneous fiber populations of fibers. A cuff electrode was used to measure activity induced by vagus nerve stimulation in in vivo porcine experiments; these measurements were compared with signals produced by the model. Main ResultsThe semi-analytic model produces signals that approximate the shape and amplitude of in vivo measurements. Partially activated fascicles contribute substantially to the signal, as eCAP contributions from smoothly varying fiber calibers in fully activated ones partially cancel. As a result, eCAP magnitude does not depend monotonically on the stimulation current and recruitment level. Because the eCAP is sensitive to the degree of activation in individual fascicles, and to the location of the recording electrodes with respect to individual fascicles, the contributions of different fascicles to the recorded eCAP signals vary significantly with changes in the shape and placement of the stimulus and the recording electrodes. SignificanceOur method can be used to rapidly assess new stimulation and recording setups involving complex nerves and neurovascular bundles, e.g., to maximize signal information content, for closed-loop control in bioelectronic medicine applications, and potentially to non-destructively reconstruct structural and functional nerve topologies through inverse problem solving.

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bioRxiv2024

A biophysically-detailed model of inter-areal interactions in cortical sensory processing

Mechanisms of top-down modulation in sensory perception and their relation to underlying connectivity are not completely understood. We present here a biophysically-detailed computational model of two interconnected cortical areas, representing the first steps in a cortical processing hierarchy, as a tool for potential discovery. The model integrates a large body of data from rodent primary somatosensory cortex and reproduces biological features across multiple scales: from a handful of ion channels defining a diversity of electrical types in hundreds of thousands of morphologically detailed neurons, to local and long-range networks mediated by hundreds of millions of synapses. Notably, long-range connectivity in the model incorporates target lamination patterns associated with feed-forward and feedback pathways. We use the model to study the impact of inter-areal interactions on sensory processing. First, we exhibit a cortico-cortical loop between the two model areas (X and Y), wherein sensory input to area X produces a response with two components in time, the first driven by the stimulus and the second by feedback from area Y. We perform a structural and functional characterization of this loop, finding a differential impact of layer-specific pathways in the feed-forward and feedback directions. Second, we explore stimulus discrimination by presenting four different spatially-segregate stimulus patterns. We observe well-defined temporal sequences of functional cell assembly activation, with stimulus specificity in early but not late assemblies in area X, i.e., in the stimulus-driven component of the response but not in the feedback-driven component. We also find the earliest assembly in area Y to be specific to pairs of patterns, consistent with the topography of connections. Finally, we examine the integration of bottom-up and top-down signals. When presenting a second stimulus coincident with the feedback-driven component, we observe an approximate linear superposition of responses. The implied lack of interaction is consistent with the stochastic and hence naive connectivity in the model, but provides a useful foundation for plasticity mechanisms to learn top-down influences. This work represents a first step in the study of inter-areal interactions with biophysically-detailed simulations.

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bioRxiv2024

A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations

Synaptic connectivity at the neuronal level is characterized by highly non-random features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But to prove causation, manipula- tions of connectivity would have to be studied. However, the fine-grained scale at which non-random trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large- scale network models in SONATA format. In addition to creating or manipulating the connectome of a model, it provides tools to fit parameters of stochastic connectivity models against existing connectomes. This enables rapid replacement of any existing connectome with equivalent connectomes at different levels of complexity, or transplantation of connectivity features from one connectome to another, for systematic study. We employed the framework in a detailed model of rat somatosensory cortex in two exemplary use cases: transplanting interneuron connectivity trends from electron microscopy data and creating simplified connectomes of excitatory connectivity. We ran a series of network simulations and found diverse shifts in the activity of individual neuron populations causally linked to these manipulations.

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bioRxiv2024

BlueRecording: A Pipeline for the efficient calculation of extracellular recordings in large-scale neural circuit models

As the size and complexity of network simulations accessible to computational neuroscience grows, new avenues open for research into extracellularly recorded electric signals. Biophysically detailed simulations permit the identification of the biological origins of the different components of recorded signals, the evaluation of signal sensitivity to different anatomical, physiological, and geometric factors, and selection of recording parameters to maximize the signal information content. Simultaneously, virtual extracellular signals produced by these networks may become important metrics for neuro-simulation validation. To enable efficient calculation of extracellular signals from large neural network simulations, we have developed BlueRecording, a pipeline consisting of standalone Python code, along with extensions to the Neurodamus simulation control application, the CoreNEURON computation engine, and the SONATA data format, to permit online calculation of such signals. In particular, we implement a general form of the reciprocity theorem, which is capable of handling non-dipolar current sources, such as may be found in long axons and recordings close to the current source, as well as complex tissue anatomy, dielectric heterogeneity, and electrode geometries. To our knowledge, this is the first application of this generalized (i.e., non-dipolar) reciprocity-based approach to simulate EEG recordings. We use these tools to calculate extracellular signals from an in silico model of the rat somatosensory cortex and hippocampus and to study signal contribution differences between regions and cell types.

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bioRxiv2024

Efficiency and reliability in biological neural network architectures

Simplified models of neural networks have demonstrated the importance of establishing a reasonable tradeoff between memory capacity and fault-tolerance in cortical coding schemes. The intensity of the tradeoff is mediated by the level of neuronal variability. Indeed, increased redundancy in neuronal activity enhances the robustness of the code at the cost of the its efficiency. We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate this tradeoff, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity, by contrasting its higher-order structure to a random control. To confirm the relevance of our metric we analyzed several openly available connectomes, revealing that they all exhibited wider distributions of simplicial complexity across subpopulations than relevant controls. Using a biologically detailed cortical model and an electron microscopic data set of cortical connectivity with co-registered functional data, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neuronal networks, due to the heterogeneity of their connectivity. Our work thus suggests an avenue for resolving seemingly paradoxical previous results that assume homogeneous connectivity.

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bioRxiv2023

Specific inhibition and disinhibition in the higher-order structure of a cortical connectome

Neurons are thought to act as parts of assemblies with strong internal excitatory connectivity. Conversely, inhibition is often reduced to blanket inhibition with no targeting specificity. We analyzed the structure of excitation and inhibition in the MICrONS mm3 dataset, an electron microscopic reconstruction of a piece of cortical tissue. We found that excitation was structured around a feed-forward flow in large non-random neuron motifs with a structure of information flow from a small number of sources to a larger number of potential targets. Inhibitory neurons connected with neurons in specific sequential positions of these motifs, implementing targeted and symmetrical competition between them. None of these trends are detectable in only pairwise connectivity, demonstrating that inhibition is structured by these large motifs. While descriptions of inhibition in cortical circuits range from non-specific blanket-inhibition to targeted, our results describe a form of targeting specificity existing in the higher-order structure of the connectome. These findings have important implications for the role of inhibition in learning and synaptic plasticity.

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bioRxiv2023

Enhancement of brain atlases with region-specific coordinate systems: flatmaps and barrel column annotations

Digital brain atlases define a hierarchy of brain regions and their locations in three-dimensional Cartesian space. They provide a standard coordinate system in which diverse datasets can be integrated for visualization and analysis. Although this coordinate system has well-defined anatomical axes, it does not provide the best context to work with the complex geometries of layered brain regions such as the neocortex. To address that, we introduce laminar coordinate systems that consider the curvature and the laminar structure of the region of interest. These new coordinate systems consist of a principal axis, locally aligned to the vertical direction and measuring depth, and two other axes that describe a flatmap, a two-dimensional representation of the horizontal extents of layers. The main property of the flatmap is that it allows seamless mapping of information back and forth between 2D and 3D spaces, in a way consistent with the principal axis. It involves a structured dimensionality reduction where information is aggregated along depth. We propose a method to enhance brain atlases with laminar coordinate systems and flatmaps based on user specifications and define a set of metrics to characterize the quality of flatmaps. We applied our method to an atlas of rat somatosensory cortex based on Paxinos and Watsons rat brain atlas, enhancing it with a laminar coordinate system adapted to the geometry of this region. Further, we applied our method to enhance the Allen Mouse Brain Atlas Common Coordinate Framework version 3 with a flatmap of the whole isocortex. We used this flatmap to produce new annotations of 33 individual barrels and barrel columns in the barrel cortex. Thanks to the properties of the flatmap, the resulting annotations are non-overlapping and follow the curvature of the cortex. Additionally, we introduced several applications highlighting the utility of laminar coordinate systems for data visualization and data-driven modeling. We provide a free software implementation of our methods for the benefit of the community.

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bioRxiv2023

Sparse and specific long-term plasticity emerge without homeostasis in a biophysically detailed cortical model

Synaptic plasticity underlies the brains ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.

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bioRxiv2023

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extend in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

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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.

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bioRxiv2023

Cortical cell assemblies and their underlying connectivity: an in silico study

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on den-drites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebbs postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs. This connectivity-based characterization of cell assemblies creates an opportunity to study plasticity at the assembly level, and beyond strictly pairwise interactions.

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bioRxiv2022

A Parcellation Scheme of Mouse Isocortex Based on Reversals in Connectivity Gradients

The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.

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