GUM*02 tutorial session UTSA, San Antonio, Texas

1. GUM*02 tutorial sessionUTSA, San Antonio, Texas Large-scale realistic modeling of neuronal networks Mike Vanier, Caltech

2. Structure of the talk: • General network modeling issues • Details of how networks are modeled in GENESIS

3. Part 1 • General network modeling issues • Details of how networks are modeled in GENESIS

4. Why model networks? • Goal: understand the brain • network of networks • Networks implement computations • influence of NN theory • Networks are where the action is!

5. Why avoid modeling networks? • networks are too complex • dozens of cell types • complex connectivities, interactions • we don’t understand neurons yet • not enough data • want to graduate quickly

6. Roots of GENESIS • GENESIS: • GEneral • NEural • SImulation • System • network modeling was orig focus

7. and yet... • most models still either • single neuron models • very small networks • “abstract” network models • maybe a 10:1 ratio or worse • why is this?

8. Network modeling is hard!!! • need accurate data on: • neuron models (ALL types) • connectivities • inputs • outputs • simplifications needed • scaling issues

9. More typical scenario • data available for some neurons only • inhibitory neurons? • connectivities only vaguely known • inputs vaguely known if at all • outputs vaguely known if at all • why bother?

10. Motivations “Abandon all hope, ye who enter here.” • more exploratory, less definitive • refine conceptual model of system • make implicit ideas about function explicit • figure out what data to collect

11. The process • collect all the data you can!!! • build simplified neuron models • match to data • build model of inputs • build network model • match to data • graduate

12. Example: piriform cortex • neuron types well established • little physiology for most • connection patterns known • inputs partially known • outputs mostly unknown

13. Neuron types

14. Simplification

15. Physiology: pyramidal neurons model real

16. Physiology: inhibitory neurons

17. inputs spike rasters ISI distribution

18. Connectivities 1 afferents

19. Connectivities 2

20. now the “fun” begins... • pick network phenomenon to model • PC: response to strong, weak shocks • independent of details of bulb • relatively simple • adjust parameters to tune model • leave neuron parameters alone • connectivities

21. results? • see my talk tomorrow • hint: I graduated

22. Part 2 • General network modeling issues • Details of how networks are modeled in GENESIS

23. GENESIS basics • modeler creates simulation objects • objects send messages to ea. other • messages contain data • field values • most messages sent each time step • or once per fixed interval • [spikes break this rule]

24. neurons • compartmental models of neurons • neuron composed of compartments • compartments are isopotential • channels connect to compartments • voltage-dependent • calcium-dependent • synaptic

25. setting up the neuron create neutral /neuron1 create compartment /neuron1/soma setfield ^ \ Em { Erest } \ // volts Rm { RM / area } \ // Ohms Cm { CM * area } \ // Farads Ra { RA * len / xarea } // Ohms

26. spikes in genesis • spikegen object • monitors Vm of compartment • when past threshold, sends SPIKE message to destination • synchan object • receives SPIKE message • stores time of spike in buffer • generates a-function when spike hits

27. setting up the synchan create synchan /neuron1/syn setfield ^ \ gmax 1.0e-9 \ // 1 nS Ek 0.0 \ tau1 0.001 \ // rise time (sec) tau2 0.003 // fall time // Connect soma to synchan: addmsg /neuron1/soma /neuron1/syn VOLTAGE Vm addmsg /neuron1/syn /neuron1/soma CHANNEL Gk Ek

28. setting up the spikegen // Create and connect spike detector: create spikegen /neuron1/spike setfield ^ thresh -0.020 abs_refract 0.002 addmsg /neuron1/soma /neuron1/spike INPUT Vm

29. connecting two neurons // Assume we have neuron2 like neuron1 addmsg /neuron1/spike /neuron2/syn SPIKE // Set synaptic weight and delay: setfield /neuron2/syn \ synapse[0].weight 1.0 \ synapse[0].delay 0.001 // 1 msec // That’s all there is to it!

30. building networks • Why not just do this for all synapses? • 100-1000 neurons, 10,000-100,000 synapses... • gets pretty tedious • faster way: large-scale connection commands • volumeconnect [planarconnect] • volumedelay [planardelay] • volumeweight [planarweight]

31. volumeconnect volumeconnect source_elements destination_elements \ -relative \ -sourcemask {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -sourcehole {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -destmask {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -desthole {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -probability p

32. volumedelay volumedelay sourcepath [destination_path] \ -fixed delay \ -radial conduction_velocity \ -add \ -uniform scale \ -gaussian stdev maxdev \ -exponential mid max \ -absoluterandom

33. volumeweight volumeweight sourcepath [destination_path] \ -fixed weight \ -decay decay_rate max_weight min_weight \ -uniform scale \ -gaussian stdev maxdev \ -exponential mid max \ -absoluterandom

34. note on connection commands • mainly useful for simple cases • more realistic cases require more control • GENESIS script language makes it easy to write own connection commands

35. output • Xodus • graphical output • dump neuron data to files • binary files readable by “xview”

36. conclusions • network modeling is • fun • fascinating • fundamental • frustrating! • NOT for the easily discouraged!