Sharon M. Crook

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.

Microsaccadic Efficacy and Contribution to Foveal and Peripheral Vision

Our eyes move constantly, even when we try to fixate our gaze. Fixational eye movements prevent and restore visual loss during fixation, yet the relative impact of each type of fixational eye movement remains controversial. For over five decades, the debate has focused on microsaccades, the fastest and largest fixational eye movements. Some recent studies have concluded that microsaccades counteract visual fading during fixation. Other studies have disputed this idea, contending that microsaccades play no significant role in vision. The disagreement stems from the lack of methods to determine the precise effects of microsaccades on vision versus those of other eye movements, as well as a lack of evidence that microsaccades are relevant to foveal vision. Here we developed a novel generalized method to determine the precise quantified contribution and efficacy of human microsaccades to restoring visibility compared with other eye movements. Our results indicate that microsaccades are the greatest eye movement contributor to the restoration of both foveal and peripheral vision during fixation. Our method to calculate the efficacy and contribution of microsaccades to perception can determine the strength of connection between any two physiological and/or perceptual events, providing a novel and powerful estimate of causal influence; thus, we anticipate wide-ranging applications in neuroscience and beyond.

MorphML: Level 1 of the NeuroML Standards for Neuronal Morphology Data and Model Specification

Quantitative neuroanatomical data are important for the study of many areas of neuroscience, and the complexity of problems associated with neuronal structure requires that research from multiple groups across many disciplines be combined. However, existing neuron-tracing systems, simulation environments, and tools for the visualization and analysis of neuronal morphology data use a variety of data formats, making it difficult to exchange data in a readily usable way. The NeuroML project was initiated to address these issues, and here we describe an extensible markup language standard, MorphML, which defines a common data format for neuronal morphology data and associated metadata to facilitate data and model exchange, database creation, model publication, and data archiving. We describe the elements of the standard in detail and outline the mappings between this format and those used by a number of popular applications for reconstruction, simulation, and visualization of neuronal morphology.

LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2.

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.

The Open Source Brain Initiative: enabling collaborative modelling in computational neuroscience

While an increasing number of biophysically detailed neuronal models (featuring (semi-) realistic morphologies and voltage and ligand gated conductances) are being shared across the community through resources like ModelDB, these usually only represent a snapshot of the model at the time of publication, in a format specific to the original simulator used. Models are constantly evolving however, to take account of new experimental findings and to address new research questions, both by the original modellers, and by other researchers who help provide quality control/debugging of original scripts and convert the model (components) for use in other simulators. This crucial part of the model life cycle is not well addressed with currently available infrastructure. The Open Source Brain (OSB) repository is being developed to provide a central location for researchers to collaboratively develop models which can be run across multiple simulators and can interact with the range of other applications in the NeuroML “ecosystem”. NeuroML [1] is a simulator independent language for expressing detailed single cell and network models, which is supported by an increasing number of applications for generating, visualising, simulating and analysing such models as well as by databases providing the base components (e.g. reconstructed morphologies, ion channels) for use in the models (http://www.neuroml. org/tool_support). The OSB repository differs from existing model databases which have traditionally concentrated on frozen, published models. The cell, ion channel, synapse and network models in this repository develop over time to ensure they reflect best practices in neurophysiological modelling and allow continuously improving, bug-free simulations. Multiple views of the model elements are available to encourage feedback from modellers, theoreticians and experimentalists. Links can be made to previous versions of the models in ModelDB, and deep links will be used to ensure cross referencing to other neuroinformatics resources such as NeuroMorpho and NeuroLex. The system is based around a Mercurial version control repository with models organised into projects illustrating a number of neurophysiologically relevant aspects of the cell and network behaviour. The history is recorded of all changes to each project by contributors who can be distributed worldwide. There is close integration with the application neuroConstruct [2], allowing the models to be examined with a 3D graphical user interface, and scripts automatically generated for use on a number of widely used neuronal simulators. A number of models are already available in the repository, including cell and network models from the cerebellum, detailed cortical and hippocampal pyramidal cell models and a 3D version of a single column thalamocortical network model [3]. While most of the models available are conversions of existing published models, some have been developed during original research projects using the tools and formats discussed here [4]. The repository is currently in alpha stage of development and is being tested with a small number of labs. The resource can be accessed at http://opensourcebrain.org:8080. This work has been primarily funded by the Wellcome Trust

Learning from the past: Approaches for reproducibility in computational neuroscience

Reproducible experiments are the cornerstone of science: only observations that can be independently confirmed enter the body of scientific knowledge. Computational science should excel in reproducibility, as simulations on digital computers avoid many of the small variations that are beyond the control of the experimental biologist or physicist. However, in reality, computational science has its own challenges for reproducibility: many computational scientists find it difficult to reproduce results published in the literature, and many authors have met problems replicating even the figures in their own papers. We present a distinction between different levels of replicability and reproducibility of findings in computational neuroscience. We also demonstrate that simulations of neural models can be highly sensitive to numerical details, and conclude that often it is futile to expect exact replicability of simulation results across simulator software packages. Thus, the computational neuroscience community needs to discuss how to define successful reproduction of simulation studies. Any investigation of failures to reproduce published results will benefit significantly from the ability to track the provenance of the original results. We present tools and best practices developed over the past 2 decades that facilitate provenance tracking and model sharing.

Relating ion channel expression, bifurcation structure, and diverse firing patterns in a model of an identified motor neuron

Neurons show diverse firing patterns. Even neurons belonging to a single chemical or morphological class, or the same identified neuron, can display different types of electrical activity. For example, motor neuron MN5, which innervates a flight muscle of adult Drosophila, can show distinct firing patterns under the same recording conditions. We developed a two-dimensional biophysical model and show that a core complement of just two voltage-gated channels is sufficient to generate firing pattern diversity. We propose Shab and DmNav to be two candidate genes that could encode these core currents, and find that changes in Shab channel expression in the model can reproduce activity resembling the main firing patterns observed in MN5 recordings. We use bifurcation analysis to describe the different transitions between rest and spiking states that result from variations in Shab channel expression, exposing a connection between ion channel expression, bifurcation structure, and firing patterns in models of membrane potential dynamics.

Fixational Eye Movement Correction of Blink-Induced Gaze Position Errors

Our eyes move continuously. Even when we attempt to fix our gaze, we produce “fixational” eye movements including microsaccades, drift and tremor. The potential role of microsaccades versus drifts in the control of eye position has been debated for decades and remains in question today. Here we set out to determine the corrective functions of microsaccades and drifts on gaze-position errors due to blinks in non-human primates (Macaca mulatta) and humans. Our results show that blinks contribute to the instability of gaze during fixation, and that microsaccades, but not drifts, correct fixation errors introduced by blinks. These findings provide new insights about eye position control during fixation, and indicate a more general role of microsaccades in fixation correction than thought previously.

Creating, documenting and sharing network models

As computational neuroscience matures, many simulation environments are available that are useful for neuronal network modeling. However, methods for successfully documenting models for publication and for exchanging models and model components among these projects are still under development. Here we briefly review existing software and applications for network model creation, documentation and exchange. Then we discuss a few of the larger issues facing the field of computational neuroscience regarding network modeling and suggest solutions to some of these problems, concentrating in particular on standardized network model terminology, notation, and descriptions and explicit documentation of model scaling. We hope this will enable and encourage computational neuroscientists to share their models more systematically in the future.

Modulation of inhibitory strength and kinetics facilitates regulation of persistent inward currents and motoneuron excitability following spinal cord injury

Spasticity is commonly observed after chronic spinal cord injury (SCI) and many other central nervous system disorders (e.g., multiple sclerosis, stroke). SCI-induced spasticity has been associated with motoneuron hyperexcitability partly due to enhanced activation of intrinsic persistent inward currents (PICs). Disrupted spinal inhibitory mechanisms also have been implicated. Altered inhibition can result from complex changes in the strength, kinetics, and reversal potential (E(Cl(-))) of γ-aminobutyric acid A (GABA(A)) and glycine receptor currents. Development of optimal therapeutic strategies requires an understanding of the impact of these interacting factors on motoneuron excitability. We employed computational methods to study the effects of conductance, kinetics, and E(Cl(-)) of a dendritic inhibition on PIC activation and motoneuron discharge. A two-compartment motoneuron with enhanced PICs characteristic of SCI and receiving recurrent inhibition from Renshaw cells was utilized in these simulations. This dendritic inhibition regulated PIC onset and offset and exerted its strongest effects at motoneuron recruitment and in the secondary range of the current-frequency relationship during PIC activation. Increasing inhibitory conductance compensated for moderate depolarizing shifts in E(Cl(-)) by limiting PIC activation and self-sustained firing. Furthermore, GABA(A) currents exerted greater control on PIC activation than glycinergic currents, an effect attributable to their slower kinetics. These results suggest that modulation of the strength and kinetics of GABA(A) currents could provide treatment strategies for uncontrollable spasms.