Summary Descriptions can be found in the program booklet. Feedback summaries provided by the workshop organizers are included here. In addition to a short NEURON course (organizers: Hines, Carnevale, Calin-Jageman, Schürmann), workshops included:

A variety of approaches are available for using computational models to help understand neural processes over many levels of description, from sub-cellular processes to behavior. Alongside purely deductive bottom-up or top-down modeling, a systems design strategy has the advantage of providing a clear goal for the behavior of a complex model. The order in which biological details are added is dictated by functional requirements in terms of the tasks the model should perform.

Background: Ion channels are proteins in the cell membrane that facilitate the di#usion of selected ions through biological membranes. Measuring ionic current has been made possible using the giga-seal patchclamp technique [13]. Modelling and simulation provide a better understanding that complements experimental results. Several tools are available. New modelling standards based on markup languages are being developed in order to promote collaborative research and model sharing. This paper presents a webbased channel simulation tool that uses a database of models described by the NeuroML language [6]. The tool currently focuses on the simulation of voltagegated ion channels and can be used as a prototype for a more generalized, standard-based and Web accessible neuronal modelling and simulation engine.

Python is emerging as a common scripting language for simulators. This opens up many possibilities for interoperability in the form of analysis, interfaces, and communications between simulators. We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. We illustrate this by using Python numerical libraries to analyze MOOSE output online, and by developing a GUI in Python/Qt for a MOOSE simulation. Finally, we build and run a composite neuronal/signaling model that uses both the NEURON and MOOSE numerical engines, and Python as a bridge between the two. Thus PyMOOSE has a high degree of interoperability with analysis routines, with graphical toolkits, and with other simulators.

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 importance of modeling and simulation of biological process is growing for further understanding of living systems at all scales from molecular to cellular, organic, and individuals. In the field of neuroscience, there are so called platform simulators, the de-facto standard neural simulators. More than a hundred neural models are registered on the model database. These models are executable in corresponding simulation environments. But usability of the registered models is not sufficient. In order to make use of the model, the users have to identify the input, output and internal state variables and parameters of the models. The roles and units of each variable and parameter are not explicitly defined in the model files. These are suggested implicitly in the papers where the simulation results are demonstrated. In this study, we propose a novel method of reassembly and interfacing models registered on biological model database. The method was applied to the neural models registered on one of the typical biological model database, ModelDB. The results are discribed in detail with the hippocampal pyramidal neuron model. The model is executable in NEURON simulator environment, which demonstrates that somatic EPSP amplitude is independent of synapse location. Input and output parameters and variables were identified successfully, and the results of the simulation were recorded in the organized form with annotations.

A need is identi ed to build models of the central nervous system that are semi-complete, applied within multiple contexts to multiple tasks, using methodologies that span multiple levels of abstraction. The issues and constraints in building such models are discussed with respect to completeness, validation, cost, scalability and robustness. An approach currently being explored is described that is suited to the creation of large heterogenous models by small independently collaborating research groups. It is based on a network model interface, a software wrapper that abstracts the interaction between a generic component and a generic framework. c ○ 2004 Published by Elsevier B.V.

# The Author(s) 2008. This article is published with open access at Springerlink.com

The importance of modeling and simulation of biological process is growing for further understanding of living systems at all scales from molecular to cellular, organic, and individuals. In the field of neuroscience, there are so called platform simulators, the de-facto standard neural simulators. More than a hundred neural models are registered on the model database. These models are executable in corresponding simulation environments. But usability of the registered models is not sufficient. In order to make use of the model, the users have to identify the input, output and internal state variables and parameters of the models. The roles and units of each variable and parameter are not explicitly defined in the model files. These are suggested implicitly in the papers where the simulation results are demonstrated. In this study, we propose a novel method of reassembly and interfacing models registered on biological model database. The method was applied to the neural models registered on one of the typical biological model database, ModelDB. The results are discribed in detail with the hippocampal pyramidal neuron model. The model is executable in NEURON simulator environment, which demonstrates that somatic EPSP amplitude is independent of synapse location. Input and output parameters and variables were identified successfully, and the results of the simulation were recorded in the organized form with annotations.

Abstract not found