Introduction to the NEURON simulator Arnd Roth Wolfson Institute for Biomedical Research University College London

Mel, 1994

How do neurons transform synaptic inputs into action potential output?

What are the functional compartments in neurons?

How do networks of neurons work? Helmstaedter et al., 2013

How do networks of neurons work?

Single neuron and network simulators NEURON GENESIS MOOSE PSICS NEST

Passive cable models: Ingredients Specific resistivity of the intracellular medium, R i = 70 to 150 Ω cm Specific capacity of the cell membrane, C m = ~1 µF cm –2 Specific membrane resistance, R m = 10 to 100 kΩ cm 2 Membrane potential V(x,t) Axial current i a (x,t) Membrane current i m (x,t)

Steady-state condition (“leaky-end” boundary)

Steady-state condition Dendritic trees Rall & Rinzel, 1973 (Rinzel & Rall, transient solution)

Steady-state attenuation of voltage in cerebellar Purkinje cells Roth & Häusser, 2001

Transient input

Dendritic democracy: EPSPs in Purkinje cells

EPSPs in pyramidal cells

Spatial and temporal summation of subthreshold synaptic potentials Rall, 1964

Backpropagation of action potentials Stuart & Sakmann, 1994

Experimental measurements of action potential backpropagation: variability between cell types Stuart, Spruston, Sakmann & Häusser, 1997 Distance from soma (µm) Normalized AP amplitude

Action potential backpropagation in simulations isolating morphology as the only variable Vetter, Roth & Häusser, 2001

Morphology determines the sensitivity of backpropagation to modulation of channel densities

Constructing equivalent cable representations

Constructing equivalent cable representations

Equivalent cables – reduced models of dendrites predicting backpropagation with high reliability

Action potential backpropagation and Purkinje cell development Original morphologies Equivalent cables

The structure of NEURON Simulation engine Scripting language for running simulations: hoc (+ Python) Mechanism description language: NMODL Graphical user interface: InterViews Extensions and interoperability (Python, NeuroML)

Compartmentalization in NEURON “section” “segment”

Compartmentalization in NEURON nseg = 2 v(0) v(0.25) v(0.75) v(1)

A sample hoc script create cable access cable L = /* micron */ diam = 1 /* micron */ nseg = 1001 insert pas g_pas = 1/20000 /* 1/(Ohm*cm^2) = Siemens/cm^2 */ e_pas = -65 /* mV */ xopen("cable.ses")

Important built-in variables in hoc t/* ms */ dt/* ms */ L/* micron */ diam/* micron */ nseg cm/* µF/cm^2 */ Ra/* Ω*cm */ g_pas/* S/cm^2 = 1/(Ω*cm^2) */ e_pas/* mV */ celsius/* °C */

NEURON documentation

An example model Mainen & Sejnowski (1996): ModelDB lDB/ShowModel.cshtml?model=24 88