Difference in axon diameter and myelin thickness between excitatory and inhibitory callosally projecting axons in mice.

Synchronization of network oscillation in spatially distant cortical areas is essential for normal brain activity. Precision in synchronization between hemispheres depends on the axonal conduction velocity, which is determined by physical parameters of the axons involved, including diameter, and extent of myelination. To compare these parameters in long-projecting excitatory and inhibitory axons in the corpus callosum, we used genetically modified mice and virus tracing to separately label CaMKIIα expressing excitatory and GABAergic inhibitory axons. Using electron microscopy analysis, we revealed that (i) the axon diameters of excitatory fibers (myelinated axons) are significantly larger than those of nonmyelinated excitatory axons; (ii) the diameters of bare axons of excitatory myelinated fibers are significantly larger than those of their inhibitory counterparts; and (iii) myelinated excitatory fibers are significantly larger than myelinated inhibitory fibers. Also, the thickness of myelin ensheathing inhibitory axons is significantly greater than for excitatory axons, with the ultrastructure of the myelin around excitatory and inhibitory fibers also differing. We generated a computational model to investigate the functional consequences of these parameter divergences. Our simulations indicate that impulses through inhibitory and excitatory myelinated fibers reach the target almost simultaneously, whereas action potentials conducted by nonmyelinated axons reach target cells with considerable delay.

EBRAINS Live Papers - Interactive Resource Sheets for Computational Studies in Neuroscience.

We present here an online platform for sharing resources underlying publications in neuroscience. It enables authors to easily upload and distribute digital resources, such as data, code, and notebooks, in a structured and systematic way. Interactivity is a prominent feature of the Live Papers, with features to download, visualise or simulate data, models and results presented in the corresponding publications. The resources are hosted on reliable data storage servers to ensure long term availability and easy accessibility. All data are managed via the EBRAINS Knowledge Graph, thereby helping maintain data provenance, and enabling tight integration with tools and services offered under the EBRAINS ecosystem.

A faster way to model neuronal circuitry.

Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.

Reproducing and quantitatively validating a biologically-constrained point-neuron model of CA1 pyramidal cells.

We have attempted to reproduce a biologically-constrained point-neuron model of CA1 pyramidal cells. The original models, developed for the Brian simulator, captured the frequency-current profiles of both strongly and weakly adapting cells. As part of the present study, we reproduced the model for different simulators, namely Brian2 and NEURON. The reproductions were attempted independent of the original Brian implementation, relying solely on the published article. The different implementations were quantitatively validated, to evaluate how well they mirror the original model. Additional tests were developed and packaged into a test suite, that helped further characterize and compare various aspects of these models, beyond the scope of the original study. Overall, we were able to reproduce the core features of the model, but observed certain unaccountable discrepancies. We demonstrate an approach for undertaking these evaluations, using the SciUnit framework, that allows for such quantitative validations of scientific models, to verify their accurate replication and/or reproductions. All resources employed and developed in our study have been publicly shared via the EBRAINS Live Papers platform.

The EBRAINS Hodgkin-Huxley Neuron Builder: An online resource for building data-driven neuron models.

In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require ad hoc programming. To address this, we developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework. Thanks to its inherent characteristics, the HHNB facilitates the data-driven model building workflow and its reproducibility, hence fostering a collaborative approach to brain modeling.

Effect of Variations in Gap Junctional Coupling on the Frequency of Oscillatory Action Potentials in a Smooth Muscle Syncytium.

Gap junctions provide pathways for intercellular communication between adjacent cells, allowing exchange of ions and small molecules. Based on the constituent protein subunits, gap junctions are classified into different subtypes varying in their properties such as unitary conductances, sensitivity to transjunctional voltage, and gating kinetics. Gap junctions couple cells electrically, and therefore the electrical activity originating in one cell can affect and modulate the electrical activity in adjacent cells. Action potentials can propagate through networks of such electrically coupled cells, and this spread is influenced by the nature of gap junctional coupling. Our study aims to computationally explore the effect of differences in gap junctional properties on oscillating action potentials in electrically coupled tissues. Further, we also explore variations in the biophysical environment by altering the size of the syncytium, the location of the pacemaking cell, as well as the occurrence of multiple pacemaking cells within the same syncytium. Our simulation results suggest that the frequency of oscillations is governed by the extent of coupling between cells and the gating kinetics of different gap junction subtypes. The location of pacemaking cells is found to alter the syncytial behavior, and when multiple oscillators are present, there exists an interplay between the oscillator frequency and their relative location within the syncytium. Such variations in the frequency of oscillations can have important implications for the physiological functioning of syncytial tissues.

HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data.

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.

Electrophysiology of Syncytial Smooth Muscle.

As in other excitable tissues, two classes of electrical signals are of fundamental importance to the functioning of smooth muscles: junction potentials, which arise from neurotransmission and represent the initiation of excitation (or in some instances inhibition) of the tissue, and spikes or action potentials, which represent the accomplishment of excitation and lead on to contractile activity. Unlike the case in skeletal muscle and in neurons, junction potentials and spikes in smooth muscle have been poorly understood in relation to the electrical properties of the tissue and in terms of their spatiotemporal spread within it. This owes principally to the experimental difficulties involved in making precise electrical recordings from smooth muscles and also to two inherent features of this class of muscle, ie, the syncytial organization of its cells and the distributed innervation they receive, which renders their biophysical analysis problematic. In this review, we outline the development of hypotheses and knowledge on junction potentials and spikes in syncytial smooth muscle, showing how our concepts have frequently undergone radical changes and how recent developments hold promise in unraveling some of the many puzzles that remain. We focus especially on computational models and signal analysis approaches. We take as illustrative examples the smooth muscles of two organs with distinct functional characteristics, the vas deferens and urinary bladder, while also touching on features of electrical functioning in the smooth muscles of other organs.

Investigation of the Syncytial Nature of Detrusor Smooth Muscle as a Determinant of Action Potential Shape.

Unlike most excitable cells, certain syncytial smooth muscle cells are known to exhibit spontaneous action potentials of varying shapes and sizes. These differences in shape are observed even in electrophysiological recordings obtained from a single cell. The origin and physiological relevance of this phenomenon are currently unclear. The study presented here aims to test the hypothesis that the syncytial nature of the detrusor smooth muscle tissue contributes to the variations in the action potential profile by influencing the superposition of the passive and active signals. Data extracted from experimental recordings have been compared with those obtained through simulations. The feature correlation studies on action potentials obtained from the experimental recordings suggest the underlying presence of passive signals, called spontaneous excitatory junction potentials (sEJPs). Through simulations, we are able to demonstrate that the syncytial organization of the cells, and the variable superposition of the sEJPs with the "native action potential", contribute to the diversity in the action potential profiles exhibited. It could also be inferred that the fraction of the propagated action potentials is very low in the detrusor. It is proposed that objective measurements of spontaneous action potential profiles can lead to a better understanding of bladder physiology and pathology.

A Method for the Analysis of AP Foot Convexity: Insights into Smooth Muscle Biophysics.

Action potential (AP) profiles vary based on the cell type, with cells of the same type typically producing APs with similar shapes. But in certain syncytial tissues, such as the smooth muscle of the urinary bladder wall, even a single cell is known to exhibit APs with diverse profiles. The origin of this diversity is not currently understood, but is often attributed to factors such as syncytial interactions and the spatial distribution of parasympathetic nerve terminals. Thus, the profile of an action potential is determined by the inherent properties of the cell and influenced by its biophysical environment. The analysis of an AP profile, therefore, holds potential for constructing a biophysical picture of the cellular environment. An important feature of any AP is its depolarization to threshold, termed the AP foot, which holds information about the origin of the AP. Currently, there exists no established technique for the quantification of the AP foot. In this study, we explore several possible approaches for this quantification, namely, exponential fitting, evaluation of the radius of curvature, triangulation altitude, and various area based methods. We have also proposed a modified area-based approach (CX,Y) which quantifies foot convexity as the area between the AP foot and a predefined line. We assess the robustness of the individual approaches over a wide variety of signals, mimicking AP diversity. The proposed (CX,Y) method is demonstrated to be superior to the other approaches, and we demonstrate its application on experimentally recorded AP profiles. The study reveals how the quantification of the AP foot could be related to the nature of the underlying synaptic activity and help shed light on biophysical features such as the density of innervation, proximity of varicosities, size of the syncytium, or the strength of intercellular coupling within the syncytium. The work presented here is directed toward exploring these aspects, with further potential toward clinical electrodiagnostics by providing a better understanding of whole-organ biophysics.

Modeling extracellular fields for a three-dimensional network of cells using NEURON.

BACKGROUND: Computational modeling of biological cells usually ignores their extracellular fields, assuming them to be inconsequential. Though such an assumption might be justified in certain cases, it is debatable for networks of tightly packed cells, such as in the central nervous system and the syncytial tissues of cardiac and smooth muscle. NEW METHOD: In the present work, we demonstrate a technique to couple the extracellular fields of individual cells within the NEURON simulation environment. The existing features of the simulator are extended by explicitly defining current balance equations, resulting in the coupling of the extracellular fields of adjacent cells. RESULTS: With this technique, we achieved continuity of extracellular space for a network model, thereby allowing the exploration of extracellular interactions computationally. Using a three-dimensional network model, passive and active electrical properties were evaluated under varying levels of extracellular volumes. Simultaneous intracellular and extracellular recordings for synaptic and action potentials were analyzed, and the potential of ephaptic transmission towards functional coupling of cells was explored. COMPARISON WITH EXISTING METHOD(S): We have implemented a true bi-domain representation of a network of cells, with the extracellular domain being continuous throughout the entire model. This has hitherto not been achieved using NEURON, or other compartmental modeling platforms. CONCLUSIONS: We have demonstrated the coupling of the extracellular field of every cell in a three-dimensional model to obtain a continuous uniform extracellular space. This technique provides a framework for the investigation of interactions in tightly packed networks of cells via their extracellular fields.

A computational model of urinary bladder smooth muscle syncytium : validation and investigation of electrical properties.

Certain smooth muscles, such as the detrusor of the urinary bladder, exhibit a variety of spikes that differ markedly in their amplitudes and time courses. The origin of this diversity is poorly understood but is often attributed to the syncytial nature of smooth muscle and its distributed innervation. In order to help clarify such issues, we present here a three-dimensional electrical model of syncytial smooth muscle developed using the compartmental modeling technique, with special reference to the bladder detrusor. Values of model parameters were sourced or derived from experimental data. The model was validated against various modes of stimulation employed experimentally and the results were found to accord with both theoretical predictions and experimental observations. Model outputs also satisfied criteria characteristic of electrical syncytia such as correlation between the spatial spread and temporal decay of electrotonic potentials as well as positively skewed amplitude frequency histogram for sub-threshold potentials, and lead to interesting conclusions. Based on analysis of syncytia of different sizes, it was found that a size of 21-cube may be considered the critical minimum size for an electrically infinite syncytium. Set against experimental results, we conjecture the existence of electrically sub-infinite bundles in the detrusor. Moreover, the absence of coincident activity between closely spaced cells potentially implies, counterintuitively, highly efficient electrical coupling between such cells. The model thus provides a heuristic platform for the interpretation of electrical activity in syncytial tissues.