Thursday, March 31
9:00 am - 5:00 pm
Thursday, March 31st will be devoted to a series of tutorials and round table discussions on Biological modeling. Tutorials will be offered by leading experts in realistic modeling and the interaction between modeling and experimental biology. Instructional materials from the tutorials will be published on the web after the meeting.
These tutorials have been confirmed so far. Please check this page later for possible additions.
Part I - Introduction to Realistic Neural Modeling for the Beginner
Part II - Getting Started with Modeling Using GENESIS
David Beeman, University of Colorado, Boulder
This tutorial will consist of two parts, approximately one hour each, which may be attended separately.
Part I of the tutorial is a general overview and introduction to compartmental cell modeling and realistic network simulation for the beginner. Although examples are drawn from GENESIS simulations, the tutorial emphasizes the general modeling approach, rather than the details of using any particular simulator.
Part II builds upon the background of Part I to describe some deails of how this approach is used to construct cell and network simulations in GENESIS. It will serve as an introduction and roadmap to a set of browser-based hands-on tutorials (available at the meeting) on programming simulations with GENESIS. This is intended to give one enough information to quickly begin creating cells and networks with GENESIS.
Michael Hines, Yale University
This tutorial will give a brief overview of the use of NEURON for realistic neural modeling, with an emphasis on recently added features. The tutorial will describe the use of the NEURON ChannelBuilder and and graphical specification of inhomogeneous channel density with the CellBuilder.
Dieter Jaeger, Emory University
This tutorial is oriented towards neuroscientists who are new to modeling, and would follow a more general introduction to neural modeling. It takes the specific example of a modeling study of the deep cerebellar nucleus neuron to focus on the issues of what data do you need before you can start modeling, how one begins the process of constructing a model, and what was learned from the model and the process of constructing it.
Jeremy Edgerton, Emory University
To understand single neuron computation it is desirable that realistic input patterns be given to model neurons in the study of the input-output function. Spike train objects can be used to generate input patterns in place of a full network model, which is often not available. The tutorial describes how spike trains can be generated as probability distributions, or read from experimental data. Correlations between spike trains can be constructed to test temporal coding by neural models. Examples and exercises, including strategies for data analysis will be given.
Avrama Blackwell, George Mason University
The goal of this tutorial is to discuss how to develop models of second messenger pathways and calcium dynamics. The first part of the tutorial explains the equations used to model bimolecular reactions, enzyme reactions, calcium release channels, calcium pumps and diffusion. The second part explains some of the GENESIS, kinetikit and Chemesis objects that implement the appropriate equations. Participants have the opportunity to implement some of these objects to create a simple model ready for simulating.
Michael Vanier, California Institute of Technology
This tutorial will cover the use of GENESIS tools for combining single cells into networks, positioning the cells in space, and specifying the connections between cells.
Michael Vanier, California Institute of Technology
Most biologically realistic simulations, especially highly realistic simulations of single neurons, have large numbers of parameters which are not strongly constrained by existing experimental data. In such cases, the modeler has to choose the parameters that cause the model to produce outputs which are as close as possible to the outputs of the real system. Doing this manually is a very tedious process: typically one parameter at a time is adjusted and the modeler sees if the simulation outputs are any closer to the desired behavior than before.
This tutorial will describe the use of the GENESIS library of parameter search objects and functions that automate the search process, allowing searches that might take months of manual work to be done in a few days of automated searches with no user intervention whatsoever.
Greg Hood, Pittsburgh Supercomputing Center (Carnegie Mellon University)
In this tutorial we will discuss the use of PGENESIS to reduce the time needed to perform lengthy simulations or multiple simulations. PGENESIS is targeted at two main areas: simulating large networks by partitioning them across multiple processors, and performing multiple concurrent simulations for tasks such as parameter searching. The tutorial will treat topics such as efficient network partitioning, synchronization issues, parallel I/O, parallel parameter searching, load balancing, scaling behavior, and debugging strategies. We will present an in-depth example of PGENESIS scripts for a large network, and for controlling a parameter search. We will also review pertinent considerations when selecting parallel hardware to run on, how to get supercomputing time for large problems, and compare PGENESIS with alternative approaches for dealing with large-scale models.
Moderator: Sharon Crook, Arizona State University
One of the main roles of the Neural Open Markup Language [http://www.neuroml.org], NeuroML is to facilitate cooperation in building, simulating, testing and publishing models of channels, neurons and networks of neurons. MorphML, which was developed as a common data format for neuroinformatic exchange of neural morphology [http://www.morphml.org], is distributed as part of NeuroML but can be used as a stand-alone application. In this workshop, contributors will provide an overview of these XML schemas and provide examples of their use in down-stream applications. We will also elicit feedback and ideas for the further development of XML specifications for modeling channels, channel distributions, and network connectivity.
Padraig Gleeson, University College London
neuroConstruct is a Java based application which facilitates development of 3D networks of neurons for both GENESIS and NEURON. Spatial positioning and connectivity of the neurons in the network can be specified. Existing cell morphologies in GENESIS, NEURON and SWC format can be imported, edited and simulations can be run in either simulator. Support for editing of channel mechanisms and placement of these on cells is included. Simulation data can be stored and later visualized in neuroConstruct. This tutorial will demonstrate the main features of the application.
For an example of some of the tutorials to be offered, see the Tutorials Presented at the GUM*02 meeting.