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

'Neurons in Action' in Action - Educational settings for simulations and tutorials using NEURON

Neurons in Action (NIA1, 2000; NIA1.5, 2004; NIA2, 2007), a set of tutorials and linked simulations, is designed to acquaint students with neuronal physiology through interactive, virtual laboratory experiments. Here we explore the uses of NIA in lecture, both interactive and didactic, as well as in the undergraduate laboratory, in the graduate seminar course, and as an examination tool through homework and problem set assignments. NIA, made with the simulator NEURON (http://www.neuron.yale.edu/neuron/), displays voltages, currents, and conductances in a membrane patch or signals moving within the dendrites, soma and/or axon of a neuron. Customized simulations start with the plain lipid bilayer and progress through equilibrium potentials; currents through single Na and K channels; Na and Ca action potentials; voltage clamp of a patch or a whole neuron; voltage spread and propagation in axons, motoneurons and nerve terminals; synaptic excitation and inhibition; and advanced topics such as channel kinetics and coincidence detection. The user asks and answers "what if" questions by specifying neuronal parameters, ion concentrations, and temperature, and the experimental results are then plotted as conductances, currents, and voltage changes. Such exercises provide immediate confirmation or refutation of the student's ideas to guide their learning. The tutorials are hyperlinked to explanatory information and to original research papers. Although the NIA tutorials were designed as a sequence to empower a student with a working knowledge of fundamental neuronal principles, we find that faculty are using the individual tutorials in a variety of educational situations, some of which are described here. Here we offer ideas to colleagues using interactive software, whether NIA or another tool, for educating students of differing backgrounds in the subject of neurophysiology.

Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial

Artificial neural networks are based on computational units that resemble basic information processing properties of biological neurons in an abstract and simplified manner. Generally, these formal neurons model an input-output behaviour as it is also often used to characterize biological neurons. The neuron is treated as a black box; spatial extension and temporal dynamics present in biological neurons are most often neglected. Even though artificial neurons are simplified, they can show a variety of input-output relations, depending on the transfer functions they apply. This unit on transfer functions provides an overview of different transfer functions and offers a simulation that visualizes the input-output behaviour of an artificial neuron depending on the specific combination of transfer functions.

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

Virtual Electrophysiology with SPatch

SPatch is an open source virtual laboratory designed to perform simulated electrophysiological experiments without the technical difficulties inherent to laboratory work. It provides the core equipment necessary for recording neuronal activity and allows the user to install the equipment, design their own protocols, prepare solutions to bathe the preparation or to fill the electrodes, and gather data. Assistance is provided for most steps with predefined components that are appropriate to a range of standard procedures. Experiments that can be performed with SPatch at present concern the study of voltage-gated channels in isolated neurons. This allows understanding the ionic mechanisms of Na+ and Ca2+ action potentials, after spike hyperpolarization, pacemaker tonic or bursting activity of neurons, delayed or sustained or adaptive firing of neurons in response to a depolarization, spontaneous depolarization of the membrane following an hyperpolarization, etc. In an educational context, the main interest of SPatch is to allow students to focus on the concepts and thought processes of electrophysiological investigation without the high equipment costs and extensive training required to perform laboratory work. It can be used to acquaint students with the relevant procedures before starting work in a real lab, or to give students an understanding of single neuron behavior and the ways it can be studied without requiring practical work. We illustrate the function and use of SPatch, explore educational issues arising from the inevitable differences between simulated and real laboratory work, and outline possible improvements.

SNNAP: A Tool for Teaching Neuroscience

Simulation tools aid in learning neuroscience by providing the student with an interactive environment to carry out simulated experiments and test hypotheses. The field of neuroscience is well suited for the use of simulation tools since nerve cell signaling can be described by mathematical equations and solved by computer. Neural signaling entails the propagation of electrical current along nerve membrane and transmission to neighboring neurons through synaptic connections. Action potentials and synaptic transmission can be simulated and results displayed for visualization and analysis. The neurosimulator SNNAP (Simulator for Neural Networks and Action Potentials) is a simulation environment that provides users with editors for model building, simulator engine and visual display editor. This paper presents several modeling examples that illustrate some of the capabilities and features of SNNAP. First, the Hodgkin-Huxley (HH) model is presented and the threshold phenomenon is illustrated. Second, small neural networks are described with HH models using various synaptic connections available with SNNAP. Synaptic connections may be modulated through facilitation or depression with SNNAP. A study of vesicle pool dynamics is presented using an AMPA receptor model. Finally, a central pattern generator model of the Aplysia feeding circuit is illustrated as an example of a complex network that may be studied with SNNAP. Simulation code is provided for each case study described and tasks are suggested for further investigation.