Fredrick W. Howell

Interoperability of Neuroscience Modeling Software: Current Status and Future Directions

Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperability between software systems therefore play an important role in making the modeling process more efficient and in ensuring that published models can be reliably and easily reused. Various forms of interoperability are possible including the development of portable model description standards, the adoption of common simulation languages or the use of standardized middleware. Each of these approaches finds applications within the broad range of current modeling activity. However more effort is required in many areas to enable new scientific questions to be addressed. Here we present the conclusions of the “Neuro-IT Interoperability of Simulators” workshop, held at the 11th computational neuroscience meeting in Edinburgh (July 19–20 2006; http://www.cnsorg.org). We assess the current state of interoperability of neural simulation software and explore the future directions that will enable the field to advance.

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.

Non-curated distributed databases for experimental data and models in neuroscience

Neuroscience is generating vast amounts of highly diverse data which is of potential interest to researchers beyond the laboratories in which it is collected. In particular, quantitative neuroanatomical data is relevant to a wide variety of areas, including studies of development, aging, pathology and in biophysically oriented computational modelling. Moreover, the relatively discrete and well-defined nature of the data make it an ideal application for developing systems designed to facilitate data archiving, sharing and reuse. At present, the only widely used forms of dissemination are figures and tables in published papers which suffer from inaccessibility and the loss of machine readability. They may also present only an averaged or otherwise selected subset of the available data. Numerous database projects are in progress to address these shortcomings. They employ a variety of architectures and philosophies, each with its own merits and disadvantages. One axis on which they may be distinguished is the degree of top-down control, or curation, involved in data entry. Here we consider one extreme of this scale in which there is no curation, minimal standardization and a wide degree of freedom in the form of records used to document data. Such a scheme has advantages in the ease of database creation and in the equitable assignment of perceived intellectual property by keeping the control of data in the hands of the experts who collected it. It does, however, require a more sophisticated infrastructure than conventional databases since the software must be capable of organizing diverse and differently documented data sets in an effective way. Several components of a software system to provide this infrastructure are now in place. Examples are presented, showing how these tools can be used to archive and publish neuronal morphology data, and how they can give an integrated view of data stored at many different sites.

NEOSIM: Portable large-scale plug and play modelling☆

NEOSIM is a new simulation framework addressed at building large scale and detailed models of the nervous system. Its essence is a set of interfaces and protocols that enable a plug and play architecture for incorporating existing simulation modules such as NEURON [4] and GENESIS [1] as well as future visualisation and data analysis modules. From the start it has been designed to exploit parallel and distributed computers to reduce simulation run times to manageable levels, without the additional modelling e!ort required for earlier publicly-available parallel simulation tools. In this paper, we present the design of the NEOSIM framework, and discuss its applicability to a range of modelling studies. 2001 Published by Elsevier Science B.V.

Axiope tools for data management and data sharing.

Many areas of biological research generate large volumes of very diverse data. Managing this data can be a difficult and time-consuming process, particularly in an academic environment where there are very limited resources for IT support staff such as database administrators. The most economical and efficient solutions are those that enable scientists with minimal IT expertise to control and operate their own desktop systems. Axiope provides one such solution, Catalyzer, which acts as flexible cataloging system for creating structured records describing digital resources. The user is able specify both the content and structure of the information included in the catalog. Information and resources can be shared by a variety of means, including automatically generated sets of web pages. Federation and integration of this information, where needed, is handled by Axiope’s Mercat server. Where there is a need for standardization or compatibility of the structures used by different researchers this can be achieved later by applying user-defined mappings in Mercat. In this way, large-scale data sharing can be achieved without imposing unnecessary constraints or interfering with the way in which individual scientists choose to record and catalog their work. We summarize the key technical issues involved in scientific data management and data sharing, describe the main features and functionality of Axiope Catalyzer and Axiope Mercat, and discuss future directions and requirements for an information infrastructure to support large-scale data sharing and scientific collaboration.

Linking computational neuroscience simulation tools—a pragmatic approach to component-based development☆

Abstract Many problems in computational neuroscience require sophisticated software systems that are beyond the development scope of a single individual or research group. Realizing such systems with minimal redundant effort requires cooperation among software developers and the adoption of design strategies and technology from the software industry. We are working on neuroscience-specific frameworks for modelling tools aimed specifically at maximising the benefits from of investment in software development by encouraging the reuse of software components and at facilitating model development by establishing shared formats for model description. The techniques employed include component technology for coupling parts of applications, XML file standards based on NeuroML for model and data exchange between applications and databases, and peer-peer web based indexing of models and modules. This paper describe progress to date in the modularization of simulation and analysis functions from NEURON, Catacomb and NEOSIM.

NeuroML for plug and play neuronal modeling

Modern software systems for simulation, database access, visualisation and data analysis, supporting distributed, extensible, evolutionary development, are designed around a small core that loads plug-in components. We have designed such a system for the neurosciences using an XML-based protocol, NeuroML, to exchange information between components. NeuroML supports high-level descriptions of data, models, references, and other types of information. We have built simulation kernel plug-ins, visualisation plug-ins, and model-description GUI plug-ins which interoperate in this framework. We describe the current status of these plug-ins and our future plans for further plug-in components.

Catalyzer: a novel tool for integrating, managing and publishing heterogeneous bioscience data

The integrative ambitions of systems biology and neuroinformatics—to construct working models of the machinery of living cells and brains—will flounder unless researchers have access to the huge amounts of diverse experimental data being collected. However, the vast majority of bioscience research data that is gathered is never made available to other researchers, partly for the want of an adequate software for annotating experimental data, and partly for social reasons (researchers are rarely rewarded for publishing the actual data sets—just for journal articles summarizing findings).

How do we get the data to build computational models

Abstract We present a new approach to building radically distributed databases of neuroscience data. It aims to make available to modelers the huge amount of useful experimental data and notes which currently sits on experimenters PCs and lab notebooks. The approach has two components. The initial phase is a user friendly desktop application which experimental neuroscientists can use to markup data and build small catalogs of their data. The second phase is a server application which acts like a “smarter Google” which is able to combine and index catalogs from multiple researchers and labs so that modelers can download local copies of data relevant to their study.

Scaling a slow-wave sleep cortical network model using NEOSIM

Abstract We describe a case study transforming a simulation model coded in sequential C++ to run in parallel under Neosim, to enable much larger compartmental network models to be run. For some network models cut down scale is sufficient; however, there are cases where network behaviour cannot be reproduced on a smaller model (e.g. Neurocomputing 32–33 (2000) 1041). The example we present is a model of slow-wave sleep oscillations. In an earlier paper (Neurocomputing 38 (2001) 1657) we outlined the design of the Neosim framework for scaling models, focussing on networks of compartmental neuron models built using existing simulation tools Neuron and Genesis. Here, we explain how a Hodgkin–Huxley network model coded in C++ for a cortical network was adapted for Neosim, and describe the experiments planned. This case study should be of interest to others considering how best to scale up existing models and interface their own coded models with other simulators.