David Beeman

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin–Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

Constructing realistic neural simulations with GENESIS.

The GEneral NEural SImulation System (GENESIS) is an open source simulation platform for realistic modeling of systems ranging from subcellular components and biochemical reactions to detailed models of single neurons, simulations of large networks of realistic neurons, and systems-level models. The graphical interface XODUS permits the construction of a wide variety of interfaces for the control and visualization of simulations. The object-oriented scripting language allows one to easily construct and modify simulations built from the GENESIS libraries of simulation components. Here, we present procedures for installing GENESIS and its supplementary tutorials, running GENESIS simulations, and creating new neural simulations.

Simulator-independent representation of ionic conductance models with ChannelDB

Abstract ChannelDB is an implementation of a database of ionic conductance models, stored in simulator-independent NeuroML format, in order to share channel models between different neural simulators. At present, ChannelDB is implemented as a stand-alone module with its own graphical user interface to the database, which is implemented with MySQL. The NeuroML development kit parser is used to create Java objects from the NeuroML format (XML) files stored in the database. These are then accessed with Java software to create simulation scripts for the particular simulator. ChannelDB with all source code and documentation may be downloaded from http://www.modelersworkspace.org .

The modeler's workspace: a distributed digital library for neuroscience

Abstract The amount of available data in the neuroscience community is growing rapidly, and is approaching the point where researchers can no longer keep up. This paper describes the development of the Modeler’s Workspace, a digital library system we are building to improve the researcher’s means of accessing, managing and sharing neuroscience data. The Workspace consists of a distributed system of heterogeneous databases, and a Java front-end for data mining and visualization. A three-tier architecture with CORBA as the middleware facilitates database federation. We discuss our design choices, and some sociological issues, such as how to encourage data submission and ensure security and quality control.

History of Neural Simulation Software

This chapter provides a brief history of the development of software for simulating biologically realistic neurons and their networks, beginning with the pioneering work of Hodgkin and Huxley and others who developed the computational models and tools that are used today. I also present a personal and subjective view of some of the issues that came up during the development of GENESIS, NEURON, and other general platforms for neural simulation. This is with the hope that developers and users of the next generation of simulators can learn from some of the good and bad design elements of the last generation. New simulator architectures such as GENESIS 3 allow the use of standard well-supported external modules or specialized tools for neural modeling that are implemented independently from the means of the running the model simulation. This allows not only sharing of models but also sharing of research tools. Other promising recent developments during the past few years include standard simulator-independent declarative representations for neural models, the use of modern scripting languages such as Python in place of simulator-specific ones and the increasing use of open-source software solutions.

Neural Modeling with GENESIS

Now that we have briefly described the numerical basis for the tutorials included in the first part of this book, we are ready to get started with running the tutorials. The tutorials included in this manual are all constructed using GENESIS, the General NEural SImulation System that has been under development in our laboratory at Caltech since 1985. This chapter is intended to introduce each of the tutorials, as well as provide a demonstration as to how to use the GENESIS graphical interface. First, however, we provide some basic information about the GENESIS system on which the tutorials are based.

Ion Channels in Bursting Neurons

The simple neuron model that we have built up over the past three chapters is what Llinas (1988) has referred to as the “Platonic Neuron.” In this idealized model, postsynaptic potentials in the dendrites (Chapter 6) propagate passively through the dendritic “cable” (Chapter 5) to the soma. Here, near the axon hillock, the summed and attenuated PSPs may activate voltage-dependent sodium and potassium channels that are very much like those found in the squid giant axon (Chapter 4). Although it was once assumed that this simplified description applied to most neurons, we now know that the situation can be much more complex.