Biology subjects

Oberlaender, M.

Publications and source records attributed to Oberlaender, M..

11 recordsLinked to original sources

bioRxiv2026

Morphoelectric properties of inhibitory neurons shift gradually and regardless of cell type along the depth of the cerebral cortex

How can we understand the enormous diversity of the GABAergic inhibitory neurons in the cerebral cortex? To address this question, we quantify the electrophysiological and morphological properties of inhibitory neurons across the depth of an entire cortical column in the rat barrel cortex. We find properties that shift gradually with the cortical depth of the cell bodies across all inhibitory neurons, regardless of their cell types. By isolating morphoelectric variations from their shifts along the cortical depth, we find that the same simple relationships between morphoelectric properties distinguish between the four main molecular cell types of inhibitory neurons at any cortical depth, and in both the rat barrel cortex and mouse primary visual cortex. We provide converging evidence from dense electron-microscopic reconstructions of inhibitory neurons in the mouse visual cortex, and observe comparable depth-dependent shifts in additional datasets from the mouse primary motor cortex and the middle temporal gyrus of the human cortex. Our findings indicate that two different sources of morphoelectric variations can account for the diversity of cortical inhibitory neurons. The first source is molecular cell type-specific, but cortical depth-independent. The second source is cortical depth-dependent, but affects inhibitory neurons similarly across all cell types. We propose that intrinsic developmental specification vs. extrinsic environmental modulation leads to such a decoupling of inhibitory type-specific properties from gradual shifts of these properties with cortical depth.

neuroscience↗

bioRxiv2025

The Cortical Output System that Controls a Single Vibrissa Muscle

What is the neural substrate that enables the cerebral cortex to control a single mystacial vibrissa and orchestrate its movement? To answer this question, we injected rabies virus into the intrinsic muscle that protracts the rat C3 vibrissa and used retrograde transneuronal transport to identify the cortical neurons that control the muscle. A surprisingly diverse set of cortical areas is the origin of disynaptic control over the motoneurons that influence the C3 protractor. More than two thirds of these layer 5 pyramidal neurons (L5PNs) are dispersed in frontal and parietal areas outside the primary motor cortex (vM1). This observation emphasizes the importance of descending commands from non-primary motor areas. More than a third of the L5PNs originate from somatosensory areas, such as the barrel field (vS1). The barrel field has been long considered a prototypic model system for studying sensory processing at the level of the cerebral cortex. Even so, we find that the number of L5PNs in vS1, and even their peak density, rivals the number and peak density of L5PNs in vM1. Thus, our results emphasize the importance of the barrel field in processing motor output. The distribution of L5PNs in vM1 and vS1 leads us to propose a new model of vibrissa protraction in which vM1 output results in protraction, and vS1 output results in reciprocal inhibition (suppression) of protraction. This paired initiation and suppression of complementary movements may be a general feature of the descending control signals from the rodent M1 and S1.

neuroscience↗

bioRxiv2024

Dissecting origins of wiring specificity in dense cortical connectomes

What are the origins of the highly specific wiring patterns that are formed by the neurons in the brain? To address this question, we introduce a method to predict the empirically observed wiring diagram - the connectome - at synaptic resolution based on dense electron-microscopic reconstructions of neural tissue. Our method generates the connectome based on the morphological properties of the neurons in combination with synaptic specificity models, whose parameters capture how neurons wire conditional on their subcellular, cellular and cell type properties. We employ simulation-based Bayesian inference to identify all values for these parameters that can generate the observed connectome. Finally, for each synapse in a dense reconstruction, our method provides quantitative measures to reveal which synaptic specificity models are necessary, sufficient and best-suited to generate it. The output of our method are experimentally testable predictions of wiring preferences from subcellular to cell type levels that could account for each synapse in dense reconstructions. We demonstrate our method on dense datasets from mouse primary visual and human temporal cortex. Strikingly, this demonstration shows that just three assumptions, with nearly the same synaptic specificity values, predict the connectivity in both datasets. Our method is openly accessible as a computational framework that includes a comprehensive workflow for the analysis of wiring specificity, and which provides users with full flexibility to define and test their hypotheses. Our method sets the stage to uncover the principles by which neural networks are organized, and to compare these principles across brain areas, species and time points.

neuroscience↗

bioRxiv2024

FOXP2-immunoreactive, corticothalamic pyramidal cells in neocortical layers 6a and 6b are tightly regulated by neuromodulatory systems

The FOXP2/Foxp2 gene is involved in fine motor control in many vertebrate species; in humans, it is one of the candidate genes thought to play a prominent role in language production. Several studies suggest that in the neocortex, Foxp2 is exclusively expressed in a subset of corticothalamic (CT) pyramidal cells (PCs) in layer 6 (L6). However, the morphological and intrinsic electrophysiological, synaptic and neuromodulatory properties of Foxp2-expressing L6 PCs remain largely unknown. Here we systematically characterise these properties for FOXP2-positive (FOXP2+) PCs across L6 in the rat somatosensory cortex. We find that L6 FOXP2+ PCs are distinct in all of these properties from those of L6 FOXP2-negative (FOXP2-) neuronal cell types. We show that L6 FOXP2+ PCs project exclusively to thalamus. In upper L6 (L6a), FOXP2+ PCs innervate either the first-order thalamus or both first and higher-order thalamic nuclei. FOXP2+ PCs in deep L6 (L6b) project almost exclusively to higher-order thalamus. Synaptic connections established by L6a and L6b FOXP2+ PCs exhibit low synaptic release probability, whereas L6 corticocortical PCs have a high release probability. Both L6a and L6b FOXP2+ PCs respond strongly to acetylcholine (ACh), which in the absence of TTX results in firing of action potential (AP) trains. Notably, L6b but not L6a FOXP2- PCs are highly sensitive to ACh. In addition, L6b FOXP2+ PCs close to the white matter border show strong responses to dopamine that develop into prolonged AP firing. Our data suggest that FOXP2 is a marker for CT PCs in L6 that are strongly controlled by neurotransmitters such as ACh and dopamine. These findings are in line with a pivotal role for both L6a and L6b CT PCs as modulators of thalamic activity.

neuroscience↗

bioRxiv2023

Interpretable modelling of input-output computations in cortex

Neurons receive input from thousands of synapses, which they transform into action potentials (APs) via their complex dendrites. How the dendritic location of these inputs, their timing, strength, and presynaptic origin impact AP output remains generally unknown. Here we demonstrate how to reveal which properties of the input causally underlie AP output, and how this input-output computation is influenced by the morphology and biophysical properties of the dendrites. For this purpose, we derive analytically tractable, interpretable models of the input-output computation that layer 5 pyramidal tract neurons (L5PTs) - the major output cell type of the cerebral cortex - perform upon sensory stimulation. We find that this input-output computation is preserved across L5PTs despite morphological and biophysical diversity. We show that three features are sufficient to explain in vivo observed sensory responses and receptive fields of L5PTs with high accuracy: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on this analytically tractable and interpretable description of the input-output computation, we show how to dissect the contributions of different input populations in thalamus and cortex to sensory responses of L5PTs. Thus, our approach provides a roadmap for revealing cellular input-output computations across different in vivo conditions. Author SummaryRevealing how synaptic inputs drive action potential output is one of the major challenges in neuroscience research. An increasing number of approaches therefore seek to combine detailed measurements at synaptic, cellular and network scales into biologically realistic brain models. Indeed, such models have started to make empirically testable predictions about the inputs that underlie in vivo observed activity patterns. However, the enormous complexity of these models generally prevents the derivation of interpretable descriptions that explain how neurons transform synaptic input into action potential output, and how these input-output computations depend on synaptic, cellular and network properties. Here we introduce an approach to reveal input-output computations that neurons in the cerebral cortex perform upon sensory stimulation. We reduce a realistic multi-scale cortex model to the minimal description that accounts for in vivo observed responses. Thereby, we identify the input-output computation that these cortical neurons perform under this in vivo condition, and we show that this computation is preserved across neurons despite morphological and biophysical diversity. Our approach provides analytically tractable and interpretable descriptions of neuronal input-output computations during specific in vivo conditions.

neuroscience↗

bioRxiv2023

Simulation-based inference for efficient identification of generative models in connectomics

Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the posterior distribution over parameters conditioned on the data) that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data. Author summaryThe brain is composed of an intricately connected network of cells--what are the principles that contribute to constructing these patterns of connectivity, and how? To answer these questions, amassing connectivity data alone is not enough. We must also be able to efficiently develop and test our ideas about the underlying connectivity principles. For example, we could simulate a hypothetical wiring rule like "neurons near each other are more likely to form connections" in a computational model and generate corresponding synthetic data. If the synthetic, simulated data resembles the real, measured data, then we have some confidence that our hypotheses might be correct. However, the proposed wiring rules usually have unknown parameters that we need to "tune" such that simulated data matches the measurements. The central challenge thus lies in finding all the potential parametrizations of a wiring rule that can reproduce the measured data, as this process is often idiosyncratic and labor-intensive. To tackle this challenge, we introduce an approach combining computational modeling in connectomics, deep learning, and Bayesian statistical inference to automatically infer a probability distribution over the model parameters likely to explain the data. We demonstrate our approach by inferring the parameters of a wiring rule in a detailed model of the rat barrel cortex and find that the inferred distribution identifies multiple data-compatible model parameters, reveals biologically plausible parameter interactions, and allows us to make experimentally testable predictions.

neuroscience↗

bioRxiv2022

BigNeuron: A resource to benchmark and predict best-performing algorithms for automated reconstruction of neuronal morphology

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.

bioinformatics↗

bioRxiv2021

Ion channel distributions in cortical neurons are optimized for energy-efficient active dendritic computations

The mammalian brain has an enormous demand for energy, which is thought to impose strong selective pressure by which the neurons evolve in ways that ensure robust function at minimal energy cost. However, which principles drive the ion channel distributions in the dendrites to implement different neuronal functions is yet unclear. Here we found that an energy-efficient generation of dendritic calcium action potentials in cortical pyramidal neurons requires a low expression of slow inactivating potassium channels. We demonstrate that this relationship between energy cost and neuronal function is independent of the dendritic morphology and the expression patterns of other ion channels that implement additional perisomatic and dendritic functions. Moreover, we found that calcium action potentials can arise from a wide spectrum of ion channel expression patterns, including configurations with high potassium channel densities in the dendrites. These configurations can account equally well for the characteristic intrinsic physiology of the pyramidal neurons. However, only configurations with low potassium channel densities in the distal dendrites are observed empirically. Thus, our findings indicate that cortical neurons do not utilize all theoretically possible ways to implement their functions, but instead select those optimized for energy-efficient active dendritic computations.

neuroscience↗

bioRxiv2021

Thalamus gates active dendritic computations in cortex during sensory processing

Perception is linked to a calcium-dependent dendritic spiking mechanism that enables the major output cells of the cerebral cortex - layer 5 pyramidal tract neurons - to combine inputs from different information streams. Which circuits activate this mechanism upon sensory input is unclear. Here we found that thalamocortical axons, which provide sensory input to cortex, target specifically the dendritic domains in pyramidal tract neurons that initiate calcium spikes. Sensory input thereby enables distal dendritic inputs preceding the stimulus to transform the first responses that leave cortex into bursts of action potentials. Thus, thalamus can drive active dendritic coupling of sensory with prestimulus information streams to modulate cortical output. Our findings indicate that thalamocortical coupling is first in a cascade of mechanisms that transform sensory input into perception.

neuroscience↗

bioRxiv2020

A Theory for the Emergence of Neocortical Network Architecture

It has become increasingly clear that the neurons in the cerebral cortex are not randomly interconnected. This wiring specificity can result from synapse formation mechanisms that interconnect neurons depending on their activity or genetically defined identity. Here we report that in addition to these synapse formation mechanisms, the structural composition of the neuropil provides a third prominent source by which wiring specificity emerges in cortical networks. This structurally determined wiring specificity reflects the packing density, morphological diversity and similarity of the dendritic and axonal processes. The higher these three factors are, the more recurrent the topology of the networks. Conversely, low density, diversity and similarity yield feedforward networks. These principles predict connectivity patterns from subcellular to network scales that are remarkably consistent with empirical observations from a rich body of literature. Thus, cortical network architectures reflect the specific morphological properties of their constituents to much larger degrees than previously thought.

neuroscience↗

bioRxiv2019

Thalamus drives two complementary input strata of the neocortex in parallel

Sensory information enters the neocortex via thalamocortical axons that define the major input layer 4. The same thalamocortical axons, however, additionally innervate the deep output layers 5/6. How such bistratification impacts cortical processing remains unknown. Here, we find a class of neurons that cluster specifically around thalamocortical axons at the layer 5/6 border. We show that these border stratum cells are characterized by extensive horizontal axons, that they receive strong convergent input from the thalamus, and that this input is sufficient to drive reliable sensory-evoked responses, which precede those in layer 4. These cells are hence strategically placed to amplify and relay thalamocortical inputs across the cortical area, for example to drive the fast onsets of cortical output patterns. Layer 4 is therefore not the sole starting point of cortical processing. Instead, parallel activation of layer 4 and the border stratum is necessary to broadcast information out of the neocortex.

neuroscience↗