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

2. Mel, 1994

3. How do neurons transform synaptic inputs into action potential output?

4. What are the functional compartments in neurons?

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

6. How do networks of neurons work?

7. Single neuron and network simulators NEURON http://www.neuron.yale.edu/neuron/http://www.neuron.yale.edu/neuron/ GENESIS https://www.genesis-sim.org/https://www.genesis-sim.org/ MOOSE http://moose.ncbs.res.in/http://moose.ncbs.res.in/ PSICS http://www.psics.org/http://www.psics.org/ NEST http://www.nest-initiative.org/http://www.nest-initiative.org/

8. 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)

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

10. Steady-state condition Dendritic trees Rall & Rinzel, 1973 (Rinzel & Rall, 1974 - transient solution)

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

12. Transient input

13. Dendritic democracy: EPSPs in Purkinje cells

14. EPSPs in pyramidal cells

15. Spatial and temporal summation of subthreshold synaptic potentials Rall, 1964

16. Backpropagation of action potentials Stuart & Sakmann, 1994

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

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

19. Morphology determines the sensitivity of backpropagation to modulation of channel densities

20. Constructing equivalent cable representations

21. Constructing equivalent cable representations

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

23. Action potential backpropagation and Purkinje cell development Original morphologies Equivalent cables

24. 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)

25. Compartmentalization in NEURON “section” “segment”

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

27. A sample hoc script create cable access cable L = 10000 /* 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")

28. 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 */

30. NEURON documentation http://www.neuron.yale.edu/neuron/static/new_doc/index.html

31. An example model Mainen & Sejnowski (1996): ModelDB https://senselab.med.yale.edu/Mode lDB/ShowModel.cshtml?model=24 88