Biology subjects

Reimann, M. W.

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

25 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.

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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.

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bioRxiv2020

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

1In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

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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.

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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.

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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.

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bioRxiv2018

Cortical Reliability Amid Noise and Chaos

Typical responses of cortical neurons to identical sensory stimuli are highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is constrained by the intrinsic variability of cortical circuits, which remains largely unexplored. Here, we quantified this intrinsic variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We found that stochastic neurotransmitter release is a critical component of this variability, which, amplified by recurrent connectivity, causes rapid chaotic divergence with a time constant on the order of 10-20 milliseconds. Surprisingly, weak thalamocortical stimuli can transiently overcome the chaos, and induce reliable spike times with millisecond precision. We show that this effect relies on recurrent cortical connectivity, and is not a simple effect of feed-forward thalamocortical input. We conclude that recurrent cortical architecture supports millisecond spike-time reliability amid noise and chaotic network dynamics, resolving a long-standing debate.

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