Reconstruction and Simulation of Neocortical Microcircuitry.

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm(3) containing ~31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ~8 million connections with ~37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies. PAPERCLIP: VIDEO ABSTRACT.

Right ventricular myxoma in a child causing right ventricular outflow tract obstruction.

Primary cardiac tumours are uncommon in the paediatric population, accounting for fewer than 0.5% of paediatric cases of cardiac disease. Right ventricular tumours, including myxomas, are particularly rare and may be asymptomatic or demonstrate varying degrees of cardiac dysfunction based on the location and size of the tumour, inducing conduction abnormalities, syncope, embolism, and potentially, sudden death. We report a rare case of right ventricular myxoma causing severe right ventricular outflow tract obstruction and surgical intervention in a paediatric patient.

Creation and implementation of a pharmacy-led meds-to-beds program at a large teaching hospital.

BACKGROUND: "Meds-to-beds" programs are a quality improvement intervention that is gaining wider implementation throughout the United States. The University of Oklahoma hospital system did not have this program and sought to implement one. There are sufficient data on the benefits of meds-to-beds programs, but there is a lack of literature on describing the development and implementation process. OBJECTIVES: The objective of this article is to describe the planning process, implementation, and barriers encountered during the organization of a pharmacy-led meds-to-beds program operating within 2 large teaching hospitals. PRACTICE DESCRIPTION: The University of Oklahoma Health Sciences Center campus has 7 colleges, multiple primary care and specialty clinics, and 2 hospitals. In addition, there are 3 on-campus outpatient pharmacies operated by the University of Oklahoma College of Pharmacy (OUCOP). PRACTICE INNOVATION: The college implemented a meds-to-beds program primarily serving 2 on-campus hospitals, The Oklahoma Children's Hospital and University of Oklahoma College of Pharmacy Medical Center. The program operated out of The Children's Pharmacy, an outpatient pharmacy located within the Children's Hospital. EVALUATION METHODS: A Plan-Do-Study-Act model was used, which allowed for adaptation in response to barriers encountered throughout the process. Frequent meetings among stakeholders were held to continuously evaluate progress (e.g., awareness and utilization of the program and prescription counts) and make necessary changes. RESULTS: Implementation of the program required changes in workflow both within the pharmacy and within the registration and discharge processes of medical teams. In addition, after the initiation of the meds-to-beds program, the daily prescription count more than doubled. The program averages 40 deliveries per day and 3 prescriptions per delivery and continues to grow, providing evidence of a successful meds-to-beds implementation. CONCLUSION: The Plan-Do-Study-Act model allowed for many adjustments to be made throughout the process, including the conversion from an opt-in to an opt-out model to increase program utilization.

Local glutamate-mediated dendritic plateau potentials change the state of the cortical pyramidal neuron.

Dendritic spikes in thin dendritic branches (basal and oblique dendrites) are traditionally inferred from spikelets measured in the cell body. Here, we used laser-spot voltage-sensitive dye imaging in cortical pyramidal neurons (rat brain slices) to investigate the voltage waveforms of dendritic potentials occurring in response to spatially restricted glutamatergic inputs. Local dendritic potentials lasted 200-500 ms and propagated to the cell body, where they caused sustained 10- to 20-mV depolarizations. Plateau potentials propagating from dendrite to soma and action potentials propagating from soma to dendrite created complex voltage waveforms in the middle of the thin basal dendrite, comprised of local sodium spikelets, local plateau potentials, and backpropagating action potentials, superimposed on each other. Our model replicated these voltage waveforms across a gradient of glutamatergic stimulation intensities. The model then predicted that somatic input resistance (Rin) and membrane time constant (tau) may be reduced during dendritic plateau potential. We then tested these model predictions in real neurons and found that the model correctly predicted the direction of Rin and tau change but not the magnitude. In summary, dendritic plateau potentials occurring in basal and oblique branches put pyramidal neurons into an activated neuronal state ("prepared state"), characterized by depolarized membrane potential and smaller but faster membrane responses. The prepared state provides a time window of 200-500 ms, during which cortical neurons are particularly excitable and capable of following afferent inputs. At the network level, this predicts that sets of cells with simultaneous plateaus would provide cellular substrate for the formation of functional neuronal ensembles.NEW & NOTEWORTHY In cortical pyramidal neurons, we recorded glutamate-mediated dendritic plateau potentials with voltage imaging and created a computer model that recreated experimental measures from dendrite and cell body. Our model made new predictions, which were then tested in experiments. Plateau potentials profoundly change neuronal state: a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to firing triggered by other afferent inputs.

Predicting brain organization with a computational model: 50-year perspective on lateral inhibition and oscillatory gating by dendrodendritic synapses.

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.

Embedded ensemble encoding hypothesis: The role of the "Prepared" cell.

We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10-20 mV, duration 200-500 ms). Our central hypothesis is that synaptically-evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic-to-action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from "resting" to "depolarized" neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200-500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit "binding" of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer-lasting Prepared ensemble of neurons. We hypothesize that "embedded ensemble encoding" may be an important organizing principle in networks of neurons.

Synaptic clusters function as odor operators in the olfactory bulb.

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.

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON.

Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Innovative Neurotechnologies. The development of these models requires new simulation technologies. We describe here the current use of the NEURON simulator with message passing interface (MPI) for simulation in the domain of moderately large networks on commonly available high-performance computers (HPCs). We discuss the basic layout of such simulations, including the methods of simulation setup, the run-time spike-passing paradigm, and postsimulation data storage and data management approaches. Using the Neuroscience Gateway, a portal for computational neuroscience that provides access to large HPCs, we benchmark simulations of neuronal networks of different sizes (500-100,000 cells), and using different numbers of nodes (1-256). We compare three types of networks, composed of either Izhikevich integrate-and-fire neurons (I&F), single-compartment Hodgkin-Huxley (HH) cells, or a hybrid network with half of each. Results show simulation run time increased approximately linearly with network size and decreased almost linearly with the number of nodes. Networks with I&F neurons were faster than HH networks, although differences were small since all tested cells were point neurons with a single compartment.

Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition.

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.

Neuronal calcium wave propagation varies with changes in endoplasmic reticulum parameters: a computer model.

Calcium (Ca²âº) waves provide a complement to neuronal electrical signaling, forming a key part of a neuron's second messenger system. We developed a reaction-diffusion model of an apical dendrite with diffusible inositol triphosphate (IP3), diffusible Ca²âº, IP3 receptors (IP3Rs), endoplasmic reticulum (ER) Ca²âº leak, and ER pump (SERCA) on ER. Ca²âº is released from ER stores via IP3Rs upon binding of IP3 and Ca²âº. This results in Ca²âº-induced-Ca²âº-release (CICR) and increases Ca²âº spread. At least two modes of Ca²âº wave spread have been suggested: a continuous mode based on presumed relative homogeneity of ER within the cell and a pseudo-saltatory model where Ca²âº regeneration occurs at discrete points with diffusion between them. We compared the effects of three patterns of hypothesized IP3R distribution: (1) continuous homogeneous ER, (2) hotspots with increased IP3R density (IP3R hotspots), and (3) areas of increased ER density (ER stacks). All three modes produced Ca²âº waves with velocities similar to those measured in vitro (approximately 50-90 µm /sec). Continuous ER showed high sensitivity to IP3R density increases, with time to onset reduced and speed increased. Increases in SERCA density resulted in opposite effects. The measures were sensitive to changes in density and spacing of IP3R hotspots and stacks. Increasing the apparent diffusion coefficient of Ca²âº substantially increased wave speed. An extended electrochemical model, including voltage-gated calcium channels and AMPA synapses, demonstrated that membrane priming via AMPA stimulation enhances subsequent Ca²âº wave amplitude and duration. Our modeling suggests that pharmacological targeting of IP3Rs and SERCA could allow modulation of Ca²âº wave propagation in diseases where Ca²âº dysregulation has been implicated.

Sparse distributed representation of odors in a large-scale olfactory bulb circuit.

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.

Prescriber and employee utilization of a health sciences center campus outpatient pharmacy: A qualitative analysis.

PURPOSE: The college of pharmacy has operated pharmacies on campus for over 26 years. Employees and patients are users of the pharmacies; however, utilization across the campus has been limited. This paper describes a process, as well as results, that was used to gather input from employees on a large university health sciences center campus on pharmacy needs and related behaviors on campus pharmacy utilization. METHODS: Two focus groups of staff and 4 focus groups of prescribers were conducted over 1 month. Participants were selected through purposive sampling via email within an academic health sciences center campus over a 1-month period. The sessions were moderated by one investigator using a preconstructed discussion guide and lasted 1 hour. Two additional investigators observed sessions for nonverbal communication; all sessions were audio recorded for subsequent transcription. An open-coding process was performed on verbatim transcripts using NVivo12. The investigator team then developed, refined, and grouped themes during subsequent group discussions. RESULTS: A total of 44 participants took part in 6 focus groups. Participants included prescribers (physicians, nurses, physician assistants) and staff (nonprescribers). Two major themes identified were (1) factors related to on-campus pharmacies and (2) qualities valued in a pharmacy. There was an equal split (8% for each group) on awareness of the on-campus pharmacies. Almost 11% of participants commented on the accessibility of a pharmacy being a quality valued in a pharmacy. CONCLUSION: Focus groups provided insights for the administration team regarding additional value-added services that would be helpful for the campus community, as well as various approaches to increase utilization of the on-campus pharmacies. Focus group methodology is an effective approach to engage employees of a large university campus to garner new ideas to enhance existing policies or services, as well as to gather thoughts on preliminary strategic plans before implementation.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance.

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.

NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.

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.

Atypically located cardiac haemangioma of the mitral valve.

We report an adolescent with a benign cardiac haemangioma with attachments exclusively to the anterior leaflet of the mitral valve. On the basis of our review of the literature, this study has not been reported previously.

Giant aneurysm of the left anterior descending coronary artery in a pediatric patient with Behcet's disease.

Behcet's disease is a rare autoimmune vasculitis characterized by oral aphthosis, genital ulcers, and ocular and cutaneous lesions. Vascular involvement usually affects the veins more commonly than the arteries, and coronary arterial involvement is extremely rare. We report an adolescent with Behcet's disease who developed a large pseudoaneurysm of the left anterior descending coronary artery requiring a coronary arterial bypass graft.

Mechanism of Cobalt-Catalyzed Heterodimerization of Acrylates and 1,3-Dienes. A Potential Role of Cationic Cobalt(I) Intermediates.

Coupling reactions of feedstock alkenes are promising, but few of these reactions are practiced industrially. Even though recent advances in the synthetic methodology have led to excellent regio- and enantioselectivies in the dimerization reactions between 1,3-dienes and acrylates, the efficiency as measured by the turnover numbers (TON) in the catalyst has remained modest. Through a combination of reaction progress kinetic analysis (RPKA) of a prototypical dimerization reaction, characterization of isolated low-valent cobalt catalyst precursors involved, several important details of the mechanism of this reaction have emerged. (i) The prototypical reaction has an induction period that requires at least two hours of stir time to generate the competent catalyst. (ii) Reduction of a Co(II) complex to a Co(I) complex, and subsequent generation of a cationic [Co(I)]+ species are responsible for this delay. (iii) Through RPKA using in situ IR spectroscopy, same excess experiments reveal inhibition by the product towards the end of the reaction and no catalyst deactivation is observed as long as diene is present in the medium. The low TON observed is most likely the result of the inherent instability of the putative cationic Co(I)-species that catalyzes the reaction. (iv) Different excess experiments suggest that the reaction is first order in the diene and zero order in the acrylate. (v) Catalyst loading experiments show that the catalyst is first order. The orders in the various regents were further confirmed by Variable Time Normalization Analysis (VTNA). (vi) A mechanism based on oxidative dimerization [via Co(I)/Co(III)-cycle] is proposed. Based on the results of this study, it is possible to increase the TON by a factor of 10 by conducting the reaction at an increased concentration of the starting materials, especially, the diene, which seems to stabilize the catalytic species.

A microbubble-sparged yeast propagation-fermentation process for bioethanol production.

BACKGROUND: Industrial biotechnology will play an increasing role in creating a more sustainable global economy. For conventional aerobic bioprocesses supplying O2 can account for 15% of total production costs. Microbubbles (MBs) are micron-sized bubbles that are widely used in industry and medical imaging. Using a fluidic oscillator to generate energy-efficient MBs has the potential to decrease the costs associated with aeration. However, little is understood about the effect of MBs on microbial physiology. To address this gap, a laboratory-scale MB-based Saccharomyces cerevisiae Ethanol Red propagation-fermentation bioethanol process was developed and analysed. RESULTS: Aeration with MBs increased O2 transfer to the propagation cultures. Titres and yields of bioethanol in subsequent anaerobic fermentations were comparable for MB-propagated and conventional, regular bubble (RB)-propagated yeast. However, transcript profiling showed significant changes in gene expression in the MB-propagated yeast compared to those propagated using RB. These changes included up-regulation of genes required for ergosterol biosynthesis. Ergosterol contributes to ethanol tolerance, and so the performance of MB-propagated yeast in fed-batch fermentations sparged with 1% O2 as either RBs or MBs were tested. The MB-sparged yeast retained higher levels of ergosteryl esters during the fermentation phase, but this did not result in enhanced viability or ethanol production compared to ungassed or RB-sparged fermentations. CONCLUSIONS: The performance of yeast propagated using energy-efficient MB technology in bioethanol fermentations is comparable to that of those propagated conventionally. This should underpin the future development of MB-based commercial yeast propagation.

Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data.

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.

Neurons carry information in the form of electrical signals. Each of these signals is too weak to detect on its own. But the combined signals from large groups of neurons can be detected using techniques called EEG and MEG. Sensors on or near the scalp detect changes in the electrical activity of groups of neurons from one millisecond to the next. These recordings can also reveal changes in brain activity due to disease. But how do EEG/MEG signals relate to the activity of neural circuits? While neuroscientists can rarely record electrical activity from inside the human brain, it is much easier to do so in other animals. Computer models can then compare these recordings from animals to the signals in human EEG/MEG to infer how the activity of neural circuits is changing. But building and interpreting these models requires advanced skills in mathematics and programming, which not all researchers possess. Neymotin et al. have therefore developed a user-friendly software platform that can help translate human EEG/MEG recordings into circuit-level activity. Known as the Human Neocortical Neurosolver, or HNN for short, the open-source tool enables users to develop and test hypotheses on the neural origin of EEG/MEG signals. The model simulates the electrical activity of cells in the outer layers of the human brain, the neocortex. By feeding human EEG/MEG data into the model, researchers can predict patterns of circuit-level activity that might have given rise to the EEG/MEG data. The HNN software includes tutorials and example datasets for commonly measured signals, including brain rhythms. It is free to use and can be installed on all major computer platforms or run online. HNN will help researchers and clinicians who wish to identify the neural origins of EEG/MEG signals in the healthy or diseased brain. Likewise, it will be useful to researchers studying brain activity in animals, who want to know how their findings might relate to human EEG/MEG signals. As HNN is suitable for users without training in computational neuroscience, it offers an accessible tool for discoveries in translational neuroscience.