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The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Awile, Kumbhar, Cornu, Dura-Bernal, King, Lupton, Magkanaris, McDougal, Newton, Pereira, Săvulescu, Carnevale, Lytton, Hines and Schürmann.)

The first compartmental computer models of brain neurons using the Rall method predicted novel and unexpected dendrodendritic interactions between mitral and granule cells in the olfactory bulb. We review the models from a 50-year perspective on the work that has challenged, supported, and extended the original proposal that these interactions mediate both lateral inhibition and oscillatory activity, essential steps in the neural basis of olfactory processing and perception. We highlight strategies behind the neurophysiological experiments and the Rall methods that enhance the ability of detailed compartmental modeling to give counterintuitive predictions that lead to deeper insights into neural organization at the synaptic and circuit level. The application of these methods to mechanisms of neurogenesis and plasticity are exciting challenges for the future.

Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research., Competing Interests: SN, DD, BC, RM, NC, MJ, CM, MH, MH, SJ No competing interests declared, (© 2020, Neymotin et al.)

Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.

Development of credible clinically-relevant brain simulations has been slowed due to a focus on electrophysiology in computational neuroscience, neglecting the multiscale whole-tissue modeling approach used for simulation in most other organ systems. We have now begun to extend the NEURON simulation platform in this direction by adding extracellular modeling. The extracellular medium of neural tissue is an active medium of neuromodulators, ions, inflammatory cells, oxygen, NO and other gases, with additional physiological, pharmacological and pathological agents. These extracellular agents influence, and are influenced by, cellular electrophysiology, and cellular chemophysiology-the complex internal cellular milieu of second-messenger signaling and cascades. NEURON's extracellular reaction-diffusion is supported by an intuitive Python-based where/who/what command sequence, derived from that used for intracellular reaction diffusion, to support coarse-grained macroscopic extracellular models. This simulation specification separates the expression of the conceptual model and parameters from the underlying numerical methods. In the volume-averaging approach used, the macroscopic model of tissue is characterized by free volume fraction -the proportion of space in which species are able to diffuse, and tortuosity- the average increase in path length due to obstacles. These tissue characteristics can be defined within particular spatial regions, enabling the modeler to account for regional differences, due either to intrinsic organization, particularly gray vs. white matter, or to pathology such as edema. We illustrate simulation development using spreading depression, a pathological phenomenon thought to play roles in migraine, epilepsy and stroke. Simulation results were verified against analytic results and against the extracellular portion of the simulation run under FiPy. The creation of this NEURON interface provides a pathway for interoperability that can be used to automatically export this class of models into complex intracellular/extracellular simulations and future cross-simulator standardization.

The olfactory bulb (OB) transforms sensory input into spatially and temporally organized patterns of activity in principal mitral (MC) and middle tufted (mTC) cells. Thus far, the mechanisms underlying odor representations in the OB have been mainly investigated in MCs. However, experimental findings suggest that MC and mTC may encode parallel and complementary odor representations. We have analyzed the functional roles of these pathways by using a morphologically and physiologically realistic three-dimensional model to explore the MC and mTC microcircuits in the glomerular layer and deeper plexiform layer. The model makes several predictions. MCs and mTCs are controlled by similar computations in the glomerular layer but are differentially modulated in deeper layers. The intrinsic properties of mTCs promote their synchronization through a common granule cell input. Finally, the MC and mTC pathways can be coordinated through the deep short-axon cells in providing input to the olfactory cortex. The results suggest how these mechanisms can dynamically select the functional network connectivity to create the overall output of the OB and promote the dynamic synchronization of glomerular units for any given odor stimulus.

Accurate estimation of action potential (AP)-related metabolic cost is essential for understanding energetic constraints on brain connections and signaling processes. Most previous energy estimates of the AP were obtained using the Na(+)-counting method, which seriously limits accurate assessment of metabolic cost of ionic currents that underlie AP conduction along the axon. Here, we first derive a full cable energy function for cortical axons based on classic Hodgkin-Huxley (HH) neuronal equations and then apply the cable energy function to precisely estimate the energy consumption of AP conduction along axons with different geometric shapes. Our analytical approach predicts an inhomogeneous distribution of metabolic cost along an axon with either uniformly or nonuniformly distributed ion channels. The results show that the Na(+)-counting method severely underestimates energy cost in the cable model by 20-70%. AP propagation along axons that differ in length may require over 15% more energy per unit of axon area than that required by a point model. However, actual energy cost can vary greatly depending on axonal branching complexity, ion channel density distributions, and AP conduction states. We also infer that the metabolic rate (i.e. energy consumption rate) of cortical axonal branches as a function of spatial volume exhibits a 3/4 power law relationship.

The olfactory bulb processes inputs from olfactory receptor neurons (ORNs) through two levels: the glomerular layer at the site of input, and the granule cell level at the site of output to the olfactory cortex. The sequence of action of these two levels has not yet been examined. We analyze this issue using a novel computational framework that is scaled up, in three-dimensions (3D), with realistic representations of the interactions between layers, activated by simulated natural odors, and constrained by experimental and theoretical analyses. We suggest that the postulated functions of glomerular circuits have as their primary role transforming a complex and disorganized input into a contrast-enhanced and normalized representation, but cannot provide for synchronization of the distributed glomerular outputs. By contrast, at the granule cell layer, the dendrodendritic interactions mediate temporal decorrelation, which we show is dependent on the preceding contrast enhancement by the glomerular layer. The results provide the first insights into the successive operations in the olfactory bulb, and demonstrate the significance of the modular organization around glomeruli. This layered organization is especially important for natural odor inputs, because they activate many overlapping glomeruli.

How the olfactory bulb organizes and processes odor inputs through fundamental operations of its microcircuits is largely unknown. To gain new insight we focus on odor-activated synaptic clusters related to individual glomeruli, which we call glomerular units. Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. The results provide new and specific theoretical and experimentally testable predictions.

The precise mechanism by which synaptic excitation and inhibition interact with each other in odor coding through the unique dendrodendritic synaptic microcircuits present in olfactory bulb is unknown. Here a scaled-up model of the mitral-granule cell network in the rodent olfactory bulb is used to analyze dendrodendritic processing of experimentally determined odor patterns. We found that the interaction between excitation and inhibition is responsible for two fundamental computational mechanisms: (1) a balanced excitation/inhibition in strongly activated mitral cells, leading to a sparse representation of odorant input, and (2) an unbalanced excitation/inhibition (inhibition dominated) in surrounding weakly activated mitral cells, leading to lateral inhibition. These results suggest how both mechanisms can carry information about the input patterns, with optimal level of synaptic excitation and inhibition producing the highest level of sparseness and decorrelation in the network response. The results suggest how the learning process, through the emergent development of these mechanisms, can enhance odor representation of olfactory bulb., (Copyright © 2014 the authors 0270-6474/14/3413701-13$15.00/0.)

Cardiac morbidity and mortality increases with the population age. To investigate the underlying pathological mechanisms, and suggest new ways to reduce clinical risks, computational approaches complementing experimental and clinical investigations are becoming more and more important. Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart. Using a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area. In particular, in agreement with experimental findings, we assumed that an ischemic episode can alter the gap junctions of the affected cells by reducing their average conductance. We extended these changes to include random fluctuations with time, and modifications in the gap junction rectifying conductive properties of cells along the edges of the ischemic area. The results demonstrate how these alterations can qualitatively give an account of all the main types of non-fatal arrhythmia observed experimentally, and suggest how premature beats can be eliminated in three different ways: a) with a relatively small surgical procedure, b) with a pharmacological reduction of the rectifying conductive properties of the gap-junctions, and c) by pharmacologically decreasing the gap junction conductance. In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments.

The functional consequences of the laminar organization observed in cortical systems cannot be easily studied using standard experimental techniques, abstract theoretical representations, or dimensionally reduced models built from scratch. To solve this problem we have developed a full implementation of an olfactory bulb microcircuit using realistic three-dimensional (3D) inputs, cell morphologies, and network connectivity. The results provide new insights into the relations between the functional properties of individual cells and the networks in which they are embedded. To our knowledge, this is the first model of the mitral-granule cell network to include a realistic representation of the experimentally-recorded complex spatial patterns elicited in the glomerular layer (GL) by natural odor stimulation. Although the olfactory bulb, due to its organization, has unique advantages with respect to other brain systems, the method is completely general, and can be integrated with more general approaches to other systems. The model makes experimentally testable predictions on distributed processing and on the differential backpropagation of somatic action potentials in each lateral dendrite following odor learning, providing a powerful 3D framework for investigating the functions of brain microcircuits.

In order to support research on the role of cell biological principles (genomics, proteomics, signaling cascades and reaction dynamics) on the dynamics of neuronal response in health and disease, NEURON's Reaction-Diffusion (rxd) module in Python provides specification and simulation for these dynamics, coupled with the electrophysiological dynamics of the cell membrane. Arithmetic operations on species and parameters are overloaded, allowing arbitrary reaction formulas to be specified using Python syntax. These expressions are then transparently compiled into bytecode that uses NumPy for fast vectorized calculations. At each time step, rxd combines NEURON's integrators with SciPy's sparse linear algebra library.

The network of interactions between mitral and granule cells in the olfactory bulb is a critical step in the processing of odor information underlying the neural basis of smell perception. We are building the first computational model in 3 dimensions of this network in order to analyze the rules for connectivity and function within it. The initial results indicate that this network can be modeled to simulate experimental results on the activation of the olfactory bulb by natural odorants, providing a much more powerful approach for 3D simulation of brain neurons and microcircuits.

In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and generation of testable hypotheses of the functional mechanisms underlying odor representation in the olfactory bulb circuit. Specifically, we demonstrate that lateral inhibition emerges within the olfactory bulb network through recurrent dendrodendritic synapses when constrained by a range of balanced excitatory and inhibitory conductances. We find that the spatio-temporal dynamics of lateral inhibition plays a critical role in building the glomerular-related cell clusters observed in experiments, through the modulation of synaptic weights during odor training. Lateral inhibition also mediates the development of sparse and synchronized spiking patterns of mitral cells related to odor inputs within the network, with the frequency of these synchronized spiking patterns also modulated by the sniff cycle.

On their long lateral dendrites, mitral cells of the olfactory bulb form dendrodendritic synapses with large populations of granule cell interneurons. The mitral-granule cell microcircuit operating through these reciprocal synapses has been implicated in inducing synchrony between mitral cells. However, the specific mechanisms of mitral cell synchrony operating through this microcircuit are largely unknown and are complicated by the finding that distal inhibition on the lateral dendrites does not modulate mitral cell spikes. In order to gain insight into how this circuit synchronizes mitral cells within its spatial constraints, we built on a reduced circuit model of biophysically realistic multi-compartment mitral and granule cells to explore systematically the roles of dendrodendritic synapse location and mitral cell separation on synchrony. The simulations showed that mitral cells can synchronize when separated at arbitrary distances through a shared set of granule cells, but synchrony is optimally attained when shared granule cells form two balanced subsets, each subset clustered near to a soma of the mitral cell pairs. Another constraint for synchrony is that the input magnitude must be balanced. When adjusting the input magnitude driving a particular mitral cell relative to another, the mitral-granule cell circuit served to normalize spike rates of the mitral cells while inducing a phase shift or delay in the more weakly driven cell. This shift in phase is absent when the granule cells are removed from the circuit. Our results indicate that the specific distribution of dendrodendritic synaptic clusters is critical for optimal synchronization of mitral cell spikes in response to their odor input.

Odors are encoded in spatio-temporal patterns within the olfactory bulb, but the mechanisms of odor recognition and discrimination are poorly understood. It is reasonable to postulate that the olfactory code is sculpted by lateral and feedforward inhibition mediated by granule cells onto the mitral cells. Recent viral tracing and physiological studies revealed patterns of distributed granule cell synaptic clusters that provided additional clues to the possible mechanisms at the network level. The emerging properties and functional roles of these patterns, however, are unknown. Here, using a realistic model of 5 mitral and 100 granule cells we show how their synaptic network can dynamically self-organize and interact through an activity-dependent dendrodendritic mechanism. The results suggest that the patterns of distributed mitral-granule cell connectivity may represent the most recent history of odor inputs, and may contribute to the basic processes underlying mixture perception and odor qualities. The model predicts how and why the dynamical interactions between the active mitral cells through the granule cell synaptic clusters can account for a variety of puzzling behavioral results on odor mixtures and on the emergence of synthetic or analytic perception.

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.

Neuroscience simulators allow scientists to express models in terms of biological concepts, without having to concern themselves with low-level computational details of their implementation. The expressiveness, power and ease-of-use of the simulator interface is critical in efficiently and accurately translating ideas into a working simulation. We review long-term trends in the development of programmable simulator interfaces, and examine the benefits of moving from proprietary, domain-specific languages to modern dynamic general-purpose languages, in particular Python, which provide neuroscientists with an interactive and expressive simulation development environment and easy access to state-of-the-art general-purpose tools for scientific computing.

The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

The mitral cell primary dendrite plays an important role in transmitting distal olfactory nerve input from olfactory glomerulus to the soma-axon initial segment. To understand how dendritic active properties are involved in this transmission, we have combined dual soma and dendritic patch recordings with computational modeling to analyze action-potential initiation and propagation in the primary dendrite. In response to depolarizing current injection or distal olfactory nerve input, fast Na(+) action potentials were recorded along the entire length of the primary dendritic trunk. With weak-to-moderate olfactory nerve input, an action potential was initiated near the soma and then back-propagated into the primary dendrite. As olfactory nerve input increased, the initiation site suddenly shifted to the distal primary dendrite. Multi-compartmental modeling indicated that this abrupt shift of the spike-initiation site reflected an independent thresholding mechanism in the distal dendrite. When strong olfactory nerve excitation was paired with strong inhibition to the mitral cell basal secondary dendrites, a small fast prepotential was recorded at the soma, which indicated that an action potential was initiated in the distal primary dendrite but failed to propagate to the soma. As the inhibition became weaker, a "double-spike" was often observed at the dendritic recording site, corresponding to a single action potential at the soma. Simulation demonstrated that, in the course of forward propagation of the first dendritic spike, the action potential suddenly jumps from the middle of the dendrite to the axonal spike-initiation site, leaving the proximal part of primary dendrite unexcited by this initial dendritic spike. As Na(+) conductances in the proximal dendrite are not activated, they become available to support the back-propagation of the evoked somatic action potential to produce the second dendritic spike. In summary, the balance of spatially distributed excitatory and inhibitory inputs can dynamically switch the mitral cell firing among four different modes: axo-somatic initiation with back-propagation, dendritic initiation either with no forward propagation, forward propagation alone, or forward propagation followed by back-propagation.

In olfactory mitral cells, dual patch recordings show that the site of action potential initiation can shift between soma and distal primary dendrite and that the shift is dependent on the location and strength of electrode current injection. We have analyzed the mechanisms underlying this shift, using a model of the mitral cell that takes advantage of the constraints available from the two recording sites. Starting with homogeneous Hodgkin-Huxley-like Na(+)-K(+) channel distribution in the soma-dendritic region and much higher sodium channel density in the axonal region, the model's channel kinetics and density were adjusted by a fitting algorithm so that the model response was virtually identical to the experimental data. The combination of loading effects and much higher sodium channel density in the axon relative to the soma-dendritic region results in significantly lower "voltage threshold" for action potential initiation in the axon; the axon therefore fires first unless the voltage gradient in the primary dendrite is steep enough for it to reach its higher threshold. The results thus provide a quantitative explanation for the stimulus strength and position dependence of the site of action potential initiation in the mitral cell.

The class of lysine-rich histones, H1, found in most eukaryotic cells is largely replaced by another class of lysine-rich histones, H5, in avian and other erythrocytes. Erythrocytes are transcriptionally inert and this state has been attributed to the presence of H5. Although there are many sequence differences between H1 and H5 both molecules have very similar structures with three well-defined domains: a flexible basic N-terminal region, an apolar globular central region and a flexible basic C-terminal region. The lengths of the N-terminal regions are different for H1 and H5 whereas the lengths of the central and C-terminal regions are very similar. Considerable interest attaches to the findings that another type of mammalian lysine-rich histone H1(0) has an apolar region exhibiting considerable sequence homology (70%) with the central globular region of H5. The abundance of H1 in cells has been found to correlate inversely with their mitotic activities. Conformational studies using high-resolution nuclear magnetic resonance and optical spectroscopy have been made of H1 and its conformational behaviour has been compared with those of H1 and H5. H1 has been found to contain a central globular region of similar size to those found in H1 and H5. However, the conformation and stability of the globular domain of H1 are very similar to the globular region of H5 rather than H1. H1 appears to be a hybrid containing a major feature of the H5 histone. The globular regions of H1 and H5 are known to bind to a specific site on the nucleosome sealing off two turns of DNA. It is proposed that H1 binds to the same site.