NAME

            CNrun -- a neuronal network simulator

SYNOPSIS

            cnrun -h | -U | -e script [OPTION ...]

DESCRIPTION

       CNrun  is  a  neuronal  network  simulator,  similar to NEURON or GENESIS, but without provision for unit
       compartments.  It reads the network topology in NeuroML format  as  exported,  f.i.,  by  neuroConstruct.
       Unit types are determined by the ‘cell_type’ attribute in the .nml definitions.

       Available neuron types, by the corresponding ‘cell_type’ string, include:

       •      HH and HHRate, conductance- and rate-based Hodgkin-Huxley neurons (Traub & Miles, 1991);

       •      A simplified but fast, fixed-dt Map neurons mimicking the HH model;

       •      Poisson, Van der Pol (VdP) and simple Pulse oscillators;

       •      synapses as described in Rall et al, 1967 (Rall) and Destexhe et al, 1994 (AB).

       Unit  parameters can be set via a set_parm_* command (see SCRIPTING below); values can be set once before
       the simulation, or continuously or periodically per user-defined schedule.

       A 6-5-order Runge-Kutta integration method is used to compute state variables.  These (membrane potential
       E  or  instantaneous firing rate R for neurons, neurotransmitter release S for synapses) as well as spike
       times can be logged.

       Scripting support in CNrun includes commands for creating and populating a model, setting parameters  for
       single  units  or groups selected based on regex matching.  Variables (‘a = 1; b = a + 2’) and arithmetic
       expressions (‘-’, ‘+’, ‘*’, ‘/’, ‘()’ ) are supported.

OPTIONS

       -C dir chdir to dir before running.

       -D     Dump all unit types in the model and exit.

       -e [script]
              Execute script.  If this option is given without a file name (or  not  given  at  all),  start  an
              interactive interpreter.

       -s     Sort units (mostly useful with verbose output).

       -L[1dbxL]
              For all listeners:

           d      Defer  writing to disk until done rather than write continuously (speeds up the simulation but
                  you can’t watch the progress live with gnuplot)

           1      Only log the first variable (appropriate for the HH model, which  units  have  in  excess  the
                  three uninteresting gating parameters).

           b      Write  in  native  binary  form  rather  than  in  ASCII.  This will speed up viewing the (now
                  label.varx files) with gnuplot.  Do your plotting with  “binary  format="%lf%lf"”  to  achieve
                  this.

                  These options can also be set using command listen_mode (which see, below).

           L      log integrator dt.

       -E double
              Listen at this interval (default 1 msec; set to 0 to listen every cycle, which can slow cnrun down
              considerably).  Also available as command listen_dt.

       -k[l|0]
              Write a model-wide log of spiking neurons, using labels (‘l’) or unit ids (‘0’).

       -e uint
              Set precision for all output (default 8).

       -iTdouble
              dt_max (default 0.5).

       -itdouble
              dt_min (default 1e-05).

       -ixdouble
              Cap dt increase by current dt value x this (default 5).

       -nc    Disable synapse coalescing (for benchmarking).

       -v int Set verbosity level (default 1; values up to 7 are meaningful).  Use a negative value to show  the
              progress percentage only, indented on the line at -8 x this value.

       -U     List all available units.

       -h     Print the overview of command-line options.

              Space  is  optional  between  the  option  letter  and argument for single-letter options.  In all
              two-letter options taking an argument though, make sure there is no space in between.

SCRIPTING

Commands are delimited by a colon or new-line. Comments are lines starting with #. The following
commands are available:

new_model NAME
Create a new model called NAME. Existing model is deleted.

use_nml NML_FILE
Load network topology from NML_FILE, creating a model if necessary, or replacing an existing
model” topology.

merge_nml NML_FILE
Merge in the topology from NML_FILE.

add_neuron TYPE LABEL
Add a new newron of type TYPE with label LABEL.

add_synapse TYPE SOURCE TARGET G
Connect the neuron labelled SOURCE to one labelled TARGET with a synapse of type TYPE, with gsyn
G.

cull_deaf_synapses
Remove synapses with zero weight.

set_parm_neuron LABEL PARM VALUE
Set parameter PARM for a specified group of neurons labelled matching LABEL.

set_parm_synapse SRC TGT PARM VALUE
Set parameter PARM for synapses between neurons labelled matching SRC and TGT. The synaptic
weight, itself not being a synapse parameter, can also be set with this command: to do this, use
‘gsyn’ as PARM.

reset Reset the model. Model time is rewound to 0 and all units have their state variables reset to
stock defaults. Any previously assigned unit parameters and attached data sources are preserved.

reset_revert_params
Reset the model. Model time is rewound to 0, all units have their state variables and parameters
reset to stock defaults.

reset_state_units REGEX
Reset the units’ as above, keeping current model time.

advance_until TIME
Advance until TIME msec.

advance TIME
Advance TIME msec.

putout REGEX
Delete units matching REGEX by label.

decimate REGEX FRAC
Randomly delete FRAC units of a population of units selected by REGEX.

start_listen REGEX
Make matching units listen.

stop_listen
Make matching units stop listening.

listen_dt [VALUE]
Set listening interval to VALUE, or show current value if VALUE not given.

listen_mode [SPEC]
Print (if argument is omitted) the current listening mode (one var only, deferred write, and/or
binary); otherwise, enable the corresponding mode if ‘1’, ‘d’ or ‘b’ occurs in SPEC, or disable it
if it does and is immediately followed by a ‘-’. Note that those units already listening will be
unaffected; to change the mode for them, issue start_listen for them after the new mode has been
set.

start_log_spikes REGEX
Make neurons matching REGEX log spikes.

stop_log_spikes REGEX
Make neurons matching REGEX stop log spikes.

sxf_params DELAY:PERIOD:SIGMA
Set spike density function initial delay, sampling period and sigma as specified.

describe_model
Print a summary of model topology and unit types.

show_units REGEX
Print parameters and state of units matching REGEX.

new_source TYPE ID ARG ...
Create a new source of type and with an id as indicated. Sources can be connected to unit
parameters as a means to set up a dynamically changing behaviour. See DYNAMIC SOURCES below.

connect_source SOURCE_ID LABEL PARM
Connect this source to matching units’ parameter.

show_sources
Show the currently active sources (both connected and idle).

exec [SCRIPT]
Execute a script. If SCRIPT not specified, start an interactive interpreter.

verbosity [LEVEL]
Set/show verbosity level.

show_vars [REGEX]
Print variables matching REGEX, or all variables if REGEX omitted.

clear_vars [REGEX]
Clear variables matching REGEX, or all if REGEX omitted.

pause [DELAY]
Pause for DELAY sec if specified, or until user presses Enter otherwise.

quit Exit current interpreter if called by exec; exit the program otherwise.

When you use the interpreter interactively, TAB will list completions approproiately, depending on the
context.

DYNAMIC SOURCES

       In addition to static unit parameter/variable assignment with set_parm_{neuron,synapse}, units can have a
       data source attached to any of their parameters or variable (even though variables will  get  overwritten
       in the next cycle).

       Data  sources are of three types (a fourth one is available for developers, an arbitrary user function of
       time, but not exposed as an interpreter command).  Where data for a source are read from a  file,  values
       are  read  using  a ‘>>’ operator (from <ifstream>) into a double variable.  The corresponding new_source
       arguments are:

       Tape FILE
              Read “time value” pairs from FILE and set the parameter’s value accordingly.

       Periodic FILE
              FILE is expected to contain, as the first number value read by  scanf("%lg"),  a  time  period  at
              which the following values are sequentially assigned to the parameter.  Values are assigned at the
              beginning of each integration cycle.

       Noise MIN:MAX
              Generate (irrespective of time) a uniformly distributed random number within MIN:MAX.

       Similarly to the parameters, state variables can also be set in this manner; in  this  case,  the  values
       read, will override whatever the inner workings of the unit assign to it.  Where a Tape has a gap between
       assignment times larger than current dt, assignments are still made; this, however,  does  not  apply  to
       Periodic sources (chiefly for performance reasons).

SYNAPSE COALESCING

       Coalesced synapses are those having identical parameters and having the same source.  Coalescing reduces,
       per divergence rate, the number of times the S variable  is  recomputed  with  identical  parameters  per
       cycle;  additionally  for  hosted  synapses,  the  integration  vector  is  shrunk to fit towards further
       performance gain.

       Coalescing happens automatically between  two  synapses  from  same  source  when,  after  all  parameter
       assignments,  they  are found to be identical (disregarding synaptic weights).  Conversely, when the user
       changes a parameter to one coalesced synapses that is  different  from  that  parameter’s  value  in  the
       others, that synapse becomes independent.

       Note  that  a synapse units’s label is dynamically formed of the label of the source with a semicolon and
       the current number of targets.  Another consequence of coalescing is that there  can  be  more  than  one
       synapse units labelled identically (hence, uniquely to identify a synapse, you need to specify its source
       and target).

       The command-line option -nc can be used to disable coalescing.

FILES

       .cnrun-history, a per-directory history of entered commands.

EXAMPLE

       In /usr/share/doc/cnrun/ratiocoding, there is a working example of cnrun setup which reproduces  some  of
       the results presented in Zavada et al (2011) PLoS paper.

BUGS

       The oscillator units other than Poisson, have not been tested.

AUTHOR

       CNRun  and  the  underlying library libcn is written by Andrei Zavada <johnhommer@gmail.com>, building on
       the original code by Thomas Nowotny, while at Sussex University in 2008-10.