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NEURON: Channel and Cell Builder

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Department of Mathematical Sciences B12412: Computational Neuroscience and Neuroinformatics NEURON http://www.neuron.yale.edu/neuron/ NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. Visit ModelDB1 for an archive of published models that are ready to run. In summary, NEURON • is a flexible and powerful simulator of neurons and networks • has important advantages over general-purpose simulators • helps users focus on important biological issues rather than purely computational concerns • has a convenient user interface • has a user-extendable library of biophysical mechanisms • has many enhancements for efficient network modeling • offers customizable initialization and simulation flow control • is widely used in neuroscience research by experimentalists and theoreticians • is well-documented and actively supported • is free, open source, and runs on (almost) everything A flexible and powerful simulator of neurons and networks. Overview of NEURON NEURON was initially designed to facilitate dealing with neuronal models in which complex membrane properties and extended geometry play important roles. Subsequently its domain of applicability has been increased by adding facilities for describing longitudinal ionic diffusion and computationally efficient representation of connections in a network. The fundamental principles behind the design and implementation of NEURON are detailed in the NEURON book2, but it is useful to summarize them briefly here. NEURON is formulated around the notion of continuous cable sections which can be connected together to form any kind of branched cable. A section can be assigned properties that vary continuously with position along its length. The aim is to completely separate the physical properties of the neuron from the numerical issue of size of spatial compartments, and thus to help the investigator focus on the biology rather than computational details. User-defined biophysical properties of membrane (e.g. ion channels, pumps) and cytoplasm (e.g. buffers and second messengers) are described in terms of differential equations, kinetic schemes, and sets of simultaneous equations. These model descriptions are 1http://senselab.med.yale.edu/senselab/modeldb/ 2The NEURON Book, Nicholas T. Carnevale, Michael L. Hines, Cambridge University Press, 2006 1 compiled, so that membrane voltage and gating states can be computed efficiently using an implicit integration method optimized for branched structures. NEURON derives its flexibility and convenience from two features. The first is a graphical interface (GUI) that can be used to create models, run initial exploratory simulations, set parameters, control common voltage and current stimuli, and graph variables as functions of time and position. The second is an object-oriented interpreter that provides a complete programming language which is useful for customization of the GUI, advanced data analysis, and optimization. Thus NEURON puts a great deal of computational power at the disposal of the user, especially for the study of models that have a close relationship to experimental data. Yet this facility as a vehicle for implementing empirically-based models immediately raises a new set of problems that are related to managing anatomical and biophysical complexity so as to achieve computational efficiency and accuracy while at the same time minimizing the effort required of the user. Cell Builder The CellBuilder is one of NEURONs latest enhancements. It is a very powerful and convenient graphical tool for constructing and managing models of individual neurons. The CellBuilder is actually a graphical code generator. In other words, you can use the CellBuilder to enter the specifications for your model cell without having to write any (hoc) code yourself. When youre satisfied with the specification you have created, the CellBuilder will write the necessary code for you. A snapshot from NEURON { CellBuilder Building a simple Hodgkin-Huxley model 1. From Build, choose Cell Builder. This creates a basic model with just a soma. 2. You will see a radio-button list - About, Topology, Subsets, Geometry, Biophysics. For now we need only concern ourselves with setting the membrane properties of the soma - choose Biophysics. 3. To give the soma Hodgkin-Huxley kinetics (the ability to generate an action potential) click hh from the list on the right. 4. The model is complete! To generate the code that NEURON will use to render the computational model click Continuous Create in the top menu. 5. We may now test the model by driving it with a current. In the main NEURON menu choose Tools -- Point Processes - Managers -- Point Manager From SelectPoint 2 Process choose your gate (IClamp) at the top of the list. Set dur (duration of stimulus) to 100 ms and amp (amplitude of stimulus) to 20 nA. 6. To plot the voltage in the cell return the main NEURON window and choose Graph -- State axis. In the window right click to bring up a context dependent menu and select Plot what? then select soma and then v(0:5) (the voltage at the mid-point of the soma). 7. To run the model return to the main NEURON window and choose Tools -- RunControl and then click Init & Run. 8. By adjusting the plotting scale (using View ...) in the Graph window you will see output. This can be changed by modifying parameters in the PointProcess Manager window. From the NEURON menu choose Tools -- Point Processes - Managers -- Point Manager and use other stimulation protocols from SelectPoint Process (such as synaptic ones: Al- phaSynapse, ExpSyn) to investigate the behaviour of the Hodgkin-Huxley model. Plot other variables of the model such as the Na current (use Plot What? -- soma -- ina(0.5)). Channel Builder The Channel Builder is a GUI tool for creating voltage- and ligand-gated channels whose state transitions are described by kinetic schemes and/or Hodgkin-Huxley-style differential equations. Channel gating can be deterministic or stochastic. Deterministic gating models are based on the (often tacit) assumption of a very large number of channels, each having a very small conductance. This is also called the "continuous system approximation to a large population of channels with discrete states." The result is a simulation in which gating variables, ionic conductances and ionic currents are continuous functions of time. The classical Hodgkin-Huxley model is a typical example of a deterministic gating model. Ionic gates are embedded in the cell membrane and control the passage of ions. Conductance based models are all written in the form of a current-balance nonlinear differential equation: dV C = I + I ; dt ion app where C is the membrane capacitance, V the membrane voltage, Iion represents all ionic mem- brane currents and Iapp represents externally injected current. Ionic currents have the generic form p q Iion = gionm h (Vion - V); 3 where p and q are integers. Here gion is the conductance of the ionic current, m; h are (ac- tivating, inactivating) gating variables, and Vion is the reversal potential. The gating variables are dynamic, and evolve in time according to nonlinear differential equations. The art of ionic current modelling is in finding the best set of gating dynamics! The conductance variables m and h take values between 0 and 1 and approach the asymptotic values m (V), and h (V) with time constants τm(V) and τh(V) respectively. Summarizing, we have that 1 1 dm dh τ (V) = m (V)- m; τ (V) = h (V)- h m dt h dt 1 1 The functions τm(V), τh(V), m (V) and h (V) are obtained from fits with experimental data. 1 1 Adding other ionic currents to NEURON The Hodgkin-Huxley kinetics can be built from scratch (and also modified) from the main NEURON menu using Build, choosing Channel Builder -- Density, and then selecting Properties -- Gate Constructor. Clicking on Properties again one can then choose HH sodium channel or HH potassium channel. The functional forms for the gating variables (such as deined by m and τm, called inf and tau by NEURON) of these channels can be modified by clicking on the Properies radio button (in the Gate Constructor) and highlighting the gate name. 1 An example: The slow T-type calcium current Inclusion of the so-called IT current can result in a burst of spikes. This current is often found in thalamic relay (TC) cells (in the thalamus). It is thought to be involved in relaying sensory information from the brainstem and periphery to the cortex. These cells have at least two modes of operation. • During wakefulness or REM sleep, the cells resting potential is around -63 mV, and a small pulse of injected current does not evoke spikes. If the cell is depolarized to around -53 mV, then the same small pulse of injected current evokes a train of spikes. • During slow wave sleep, the cells resting potential is around -75 mV. In this mode (some- times called burst mode), the same small pulse of injected current initiates a low-threshold Ca spike (via the IT current), which results in a burst of spikes. A movie of intracellular recordings from a thalamic relay cell can be found at the McCormick lab http://www.med.yale.edu/neurobio/mccormick/movies/rly exp.mpg A simple model of this current is given by 3 IT = gTm h(VT - V); with 1 m (V) = ; τ (V) = 0:1 1 + e-(V+72)=3) m 1 h1(V) = ; τ (V) = 7:66 + 0:02868e-0:1054V ; 1 + e2(V+70) h 1 -2 with gT = 0:00013 Scm and VT = 120 mV. Run NEURON and then perform the following tasks 4 1. From Build, choose Channel Builder -- Density.
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