Spindle burst like spike discharge oscillations in organotypic cultures of neonatal rat cortex

Early network oscillations and spindle bursts are typical patterns of spontaneous rhythmic activity in cortical networks of neonatal rodents in vivo and in vitro. The latter can also be triggered in vivo by stimulation of afferent inputs. The mechanisms underlying such oscillations undergo profound developmental changes in the first postnatal weeks. Their possible role in cortical development is postulated but not known in detail. We have studied spontaneous and evoked patterns of activity in organotypic cultures of slices from neonatal rat cortex grown on multielectrode arrays (MEAs) for extracellular single- and multi-unit recording. Episodes of spontaneous spike discharge oscillations at 7 - 25 Hz lasting for 0.6 - 3 seconds appeared in about half of these cultures spontaneously and could be triggered by electrical stimulation of few distinct electrodes. These oscillations usually covered only restricted areas of the slices. Besides oscillations, single population bursts that spread in a wavelike manner over the whole slice also appeared spontaneously and were triggered by electrical stimulation. In most but not all cultures, population bursts preceded the oscillations. Both population bursts and spike discharge oscillations required intact glutamatergic synaptic transmission since they were suppressed by the AMPA/kainate glutamate receptor antagonist CNQX. The NMDA antagonist d-APV suppressed the oscillations but not the population bursts, suggesting an involvement of NMDA receptors in the oscillations. These findings show that spindle burst like cortical rhythms are reproduced in organotypic cultures of neonatal cortex. The culture model thus allows investigating the role of such rhythms in cortical circuit formation. Supported by SNF grant No. 3100A0-107641/1.

Introduction to Realistic Neural Modeling

This document corresponds to the tutorial on realistic neural modeling given by David Beeman at WAM-BAMM*05, the first annual meeting of the World Association of Modelers (WAM) Biologically Accurate Modeling Meeting (BAMM) on March 31, 2005 in San Antonio, TX. Part I - Introduction to Realistic Neural Modeling for the Beginner: This 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 - Getting Started with Modeling Using GENESIS: This builds upon the background of Part I to describe some details of how this approach is used to construct cell and network simulations in GENESIS. It serves as an introduction and roadmap to the extended hands-on GENESIS Modeling Tutorial.

GENESIS Modeling Tutorial

This tutorial is intended to be a "quick start" to creating simulations with GENESIS. It should give you the tools and enough information to let you quickly begin creating cells and networks with GENESIS, making use of the provided example simulations. Advanced topics are covered by appropriate links to the Advanced Tutorials on Realistic Neural Modeling.

Recent Developments in NEURON

We describe four recent additions to NEURON's suite of graphical tools that make it easier for users to create and manage models: an enhancement to the Channel Builder that facilitates the specification and efficient simulation of stochastic channel models

Realistic Single Cell Modeling – from Experiment to Simulation

This tutorial gives a step by step explanation of how one uses experimental data to construct a biologically realistic multicompartmental model. Special emphasis is given on the many ways that this process can be imprecise. The tutorial is intended for both experimentalists who want to get into computer modeling and for computer scientists who use abstract neural network models but are curious about biological realistic modeling. The tutorial is not dependent on the use of a specific simulation engine, but rather covers the kind of data needed for constructing a model, how they are used, and potential pitfalls in the process.

Modeling Calcium Concentration and Biochemical Reactions

Almost all regions of the brain receive one or more neuromodulatory inputs, and disrupting these inputs produces deficits in neuronal function. Neuromodulators act through intracellular second messenger pathways to influence the electrical properties of neurons, integration of synaptic inputs, spatio-temporal firing dynamics of neuronal networks, and, ultimately, systems behavior. Second messengers pathways consist of series of bimolecular reactions, enzymatic reactions, and diffusion. Calcium is the second messenger molecule with the most effectors, and thus is highly regulated by buffers, pumps and intracellular stores. Computational modeling provides an innovative, yet practical method to evaluate the spatial extent, time course and interaction among second messenger pathways, and the interaction of second messengers with neuron electrical properties. These processes occur both in compartments where the number of molecules are large enough to describe reactions deterministically (e.g. cell body), and in compartments where the number of molecules is small enough that reactions occur stochastically (e.g. spines). – In this tutorial, I explain 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. In depth explanation of calcium and second messenger models is provided by reviewing code, both in XPP, Chemesis and Kinetikit, that implements simple models of calcium dynamics and second messenger cascades.

Simulating in vivo-like Synaptic Input Patterns in Multicompartmental Models

In the laboratory of Dr. Dieter Jaeger at Emory University, we use computer simulations to study how the biophysical properties of neurons—including their three-dimensional structure, passive membrane resistance and capacitance, and active membrane conductances generated by ion channels—affect the way that the neurons transfer synaptic inputs into the action potential streams that represent their output. Because our ultimate goal is to understand how neurons process and relay information in a living animal, we try to make our computer simulations as realistic as possible. As such, the computer models reflect the detailed morphology and all of the ion channels known to exist in the particular neuron types being simulated, and the model neurons are tested with synaptic input patterns that are intended to approximate the inputs that real neurons receive in vivo. The purpose of this workshop tutorial was to explain what we mean by ‘in vivo-like’ synaptic input patterns, and how we introduce these input patterns into our computer simulations using the freely available GENESIS software package (http://www.genesis-sim.org/GENESIS). The presentation was divided into four sections: first, an explanation of what we are talking about when we refer to in vivo-like synaptic input patterns

Using P-GENESIS for Parallel Simulation of GENESIS Models

P-GENESIS is an extension to the GENESIS neural simulator that allows users to take advantage of parallel machines to speed up the simulation of their network models or concurrently simulate multiple models. P-GENESIS adds several commands to the GENESIS script language that let a script running on one processor execute remote procedure calls on other processors, and that let a script synchronize its execution with the scripts running on other processors. We present here some brief comments on the mechanisms underlying parallel script execution. We also offer advice on parallelizing parameter searches, partitioning network models, and selecting suitable parallel hardware on which to run P-GENESIS.

XML for Model Specification in Neuroscience

One of the main roles of the Neural Open Markup Language, 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 format for exchange of neural morphology data, is distributed as part of NeuroML but can be used as a stand-alone application. In this collection of tutorials and workshop summary, we provide an overview of these XML schemas and provide examples of their use in down-stream applications. We also summarize plans for the further development of XML specifications for modeling channels, channel distributions, and network connectivity.

Neural Networks as Cybernetic Systems - Part I

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology.

Neural Networks as Cybernetic Systems - Part II

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology.

Understanding Neural Maps with Topographica

The biological function of neurons can often be understood only in the context of large, highly interconnected networks. These networks typically form two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations of these areas have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered due to the lack of appropriate simulation tools. This paper introduces the freely available Topographica maplevel simulator, originally developed at the University of Texas at Austin and now maintained at the University of Edinburgh, UK. Topographica is designed to make large-scale, detailed models practical. The goal is to allow neuroscientists and computational scientists to work together to understand how topographic maps and their connections organize and operate. This understanding will be crucial for integrating experimental observations into a comprehensive theory of brain function.