• SIMULATION CONTROL I: Basic Concepts
  • Simulation management
  • Aspects of big simulations
  • Types of big simulations
  • WYSIWYNTS
  • The mod interface
  • The hoc interface
  • My tool box (in addition to NEURON/hoc/mod)
  • Simulation samsara: the eternal cycle of simulation and regret
  • Need to maintain correspondence among different versions of the same\
  • Other solutions: run traces as you type
  • Second easiest solution
  • Snapshot: what files do we need to save?
  • Version control
  • RCS (Revision Control System)
  • VC Snapshots:
  • Developing an index file
  • SIMULATION CONTROL II: Comparing and contrasting
  • Sims are computer experiments (cf Wolfram -- Scientific American)
  • Using vectors for data storage, data analysis and graphics
  • grvec.hoc provides a bunch of shortcuts using basic vec calls
  • SIMCTRL automates simulation control
  • Don't type in the simulator unless trace ongoing
  • Parameter comparisons
  • NETWORK SIMULATIONS
  • \gref{dmssl}
  • \gref{synquery2}
  • \gref{msyns}
  • Basic netcon call very simple: new NetCon(source,dest)
  • Traub defines his CA3 networks by connection probabilities
  • Number of synapses is product of convergence and number of postsynaptic cells
  • define cell names symbolically
  • If define randomly according to convergence need to balance divergence
  • Also need to balance synaptic weights -- might move to absolute conv numbers
  • Synapse routines (from syncode.hoc)
  • Thalamus model is slightly more complicated
  • 3D pmat since define columns as well
  • Need graphics to understand what you've got -- still difficult (GIGO -> GICGO)
  • Dentate gyrus model has 3 cell types
  • Pmat reflects 3 cell types
  • With a 268 line network.hoc file, very nice to have outline mode
  • Use grvec.hoc to look at results graphically
  • Raster plot
  • Summate to get population activity (can smooth)
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    SIMULATION CONTROL I: Basic Concepts (TOC)


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    Simulation management (TOC)


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    Aspects of big simulations (TOC)


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    Types of big simulations (TOC)


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    WYSIWYNTS (TOC)


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    The mod interface (TOC)


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    The hoc interface (TOC)


    Next Slide (Previous)




















































    My tool box (in addition to NEURON/hoc/mod) (TOC)


    Next Slide (Previous)




















































    Simulation samsara: the eternal cycle of simulation and regret (TOC)


    Next Slide (Previous)




















































    Need to maintain correspondence among different versions of the same virtual reality: (TOC)


    Next Slide (Previous)




















































    Other solutions: run traces as you type (TOC)


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    Second easiest solution (TOC)


    Next Slide (Previous)




















































    Snapshot: what files do we need to save? (TOC)


    Next Slide (Previous)




















































    Version control (TOC)


    Next Slide (Previous)




















































    RCS (Revision Control System) (TOC)


    Next Slide (Previous)




















































    VC Snapshots: (TOC)


    Next Slide (Previous)




















































    Developing an index file (TOC)


    Next Slide (Previous)




















































    SIMULATION CONTROL II: Comparing and contrasting (TOC)


    Next Slide (Previous)




















































    Sims are computer experiments (cf Wolfram -- Scientific American) (TOC)


    Next Slide (Previous)




















































    Using vectors for data storage, data analysis and graphics (TOC)


    Next Slide (Previous)




















































    grvec.hoc provides a bunch of shortcuts using basic vec calls (TOC)


    Next Slide (Previous)




















































    SIMCTRL automates simulation control (TOC)


    Next Slide (Previous)




















































    Don't type in the simulator unless trace ongoing (TOC)


    Next Slide (Previous)




















































    Parameter comparisons (TOC)


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    NETWORK SIMULATIONS (TOC)

    (TOC)


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    (TOC)


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    (TOC)


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    Basic netcon call very simple: new NetCon(source,dest) (TOC)


    Next Slide (Previous)




















































    Traub defines his CA3 networks by connection probabilities (TOC)


    Next Slide (Previous)




















































    Number of synapses is product of convergence and number of postsynaptic cells (TOC)


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    define cell names symbolically (TOC)

    ctypes = 2 // number of cell types
    PYR = 0
    IN1 = 1
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    If define randomly according to convergence need to balance divergence (TOC)


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    Also need to balance synaptic weights -- might move to absolute conv numbers (TOC)

    for ltr(XO,ncl) {
    XO.threshold = 0
    XO.weight = gvari(5/numdg,0.1)
    XO.delay = gvari(10,0.5)
    }
    //** vari returns randomly chosen $1+/-$2
    func uvari () { return $1 - $1*$2 + (u_rand() * 2 * $1*$2) }
    func gvari () { return $1 - (norm() * $1*$2) }
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    Synapse routines (from syncode.hoc) (TOC)


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    Thalamus model is slightly more complicated (TOC)


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    3D pmat since define columns as well (TOC)

    //* Connectivity matrix
    {TC=0 RE=1 CTYPES=2}
    dist = 3
    double pmat[CTYPES][CTYPES][2*dist+1]
    fseed(2312951)
    // TC -> RE
    pmat[TC][RE][dist] = 1
    pmat[TC][RE][dist+1] = 0.67
    pmat[TC][RE][dist-1] = 0.67
    pmat[TC][RE][dist+2] = 0.34
    pmat[TC][RE][dist-2] = 0.34
    pmat[TC][RE][dist+3] = 0.15
    pmat[TC][RE][dist-3] = 0.15
    // RE -> TC
    pmat[RE][TC][dist] = 1
    pmat[RE][TC][dist+1] = 0.67
    pmat[RE][TC][dist-1] = 0.67
    pmat[RE][TC][dist+2] = 0.34
    pmat[RE][TC][dist-2] = 0.34
    pmat[RE][TC][dist+2] = 0.15
    pmat[RE][TC][dist-2] = 0.15
    // RE -> RE
    pmat[RE][RE][dist] = 0
    pmat[RE][RE][dist+1] = 1.0
    pmat[RE][RE][dist-1] = 1.0
    pmat[RE][RE][dist+2] = 0.5
    pmat[RE][RE][dist-2] = 0.5
    pmat[RE][RE][dist+3] = 0
    pmat[RE][RE][dist-3] = 0
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    Need graphics to understand what you've got -- still difficult (GIGO -> GICGO) (TOC)


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    Dentate gyrus model has 3 cell types (TOC)


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    Pmat reflects 3 cell types (TOC)

    pmat[DG][AC] = 2/5 // Feedforward inhib
    pmat[DG][MC] = 1/20 // Varying this parameter now
    pmat[AC][MC] = 1/10 // too much input so changed
    pmat[MC][DG] = 1/20 // MC gets input from many dg's initial 1/20
    pmat[MC][AC] = 1/5 // Each ac projects to 4 mc
    pmat[AC][AC] = 1/5 // Inhib to inhib
    pmat[AC][DG] = 1/100 // Each Ac gets 1/100th input from dg's
    pmat[DG][DG] = 0 // turn off sprouting
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    With a 268 line network.hoc file, very nice to have outline mode (TOC)

    eg network.hoc
    Next Slide (Previous)




















































    Use grvec.hoc to look at results graphically (TOC)


    Next Slide (Previous)




















































    Raster plot (TOC)


    Next Slide (Previous)




















































    Summate to get population activity (can smooth) (TOC)


    Next Slide (Previous)






















































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    Last updated: Aug 8, 2000 (18:13)