mainmodel.cfg

[GENERAL]
#prefix for all directories
prefix				= './'
#Directories which will be add before importing python modules
pyextrapath			= (@GENERAL:prefix@+'/lib',@GENERAL:prefix@+'/cells')
# sets name of network file
networkfilename		= @GENERAL:prefix@+'/network.pkl'

[AUDITORY NERVE]
# auditory nerve configuration
# it is a list or tuple with two sublists of sub-tuples for 
# left and right ears correspondingly. Each sublist should have
# at least one element. 
# The first element of each sublist is a friequecy of hair cell, all
# others are types of fibers.
# EXAMPLE:
#  two hair cells at both sides with three fibers type I, II and III for
#  left ear and type III for right
# anconfig=[
#	#Left ear
#	[
#		[230, 1, 2, 3],
#		[250, 1, 2, 3]
#	],
#	[
#		[230, 3, 3, 3],
#		[250, 3, 3, 3]
#	]
#	]
# if option anconfig equal to the None, other options in AUDITORY NERVE section will be used.
# if option anconfig is string, it should be a file name with configuration.
#  script tries to read this file first and if fail int uses  AUDITORY NERVE section
#  to regenerate auditory nervous configuration.
anconfig			= "auditory_nerve.pkl"

# number of hair cells
nhcell				= 3
# 'cell distribution' should be a function from one variable 
# which is go from 0 to nhcell. 'cell distribution' must
# returns frequency in Hertz.
# Example 
#    uniformly distributed hair cells in range 5800,6400 Hz
# cell distribution		= lambda x: x/@AUDITORY NURVOUS:nhcell@*(6400-5800)+5800
# Example :
#    log10 distributed hair cells in range 5800,6400 Hz
cell distribution	= lambda x: np.logspace(np.log10(5800),np.log10(6400),$AUDITORY NERVE:nhcell$)[x]
# number of fibber per one hair cell
nfibperhcell		= 4

# XXX
fiber distribution	= lambda x,y: np.random.choice([1,2,3])
# if option file is set, script tries to read this file first and if fail
# regenerate auditory nerve 'geometry'. 
# if option wasn't set, script will regenerate geometry every time
file				= @AUDITORY NERVE:anconfig@


[STIMULI]
# stimulus parameters
# SHOULD HAVE OPTIONS:
# prog - program name to generate pkl/spkl files with stimulus
#	EXAMPLE: prog		= './an-response-generator'
# stimuli - list or tuple of option names which correspond to stimulation.
#	EXAMPLE: stimuli	= ['click-itd-100us','click-itd-000us','click-itd+100us']

# directory for saving spkl files
stimdir		= @GENERAL:prefix@+'/Dataset/'
outputdir	= @GENERAL:prefix@+'/Results/'

# list of stimulus which will be used in this experiment
stimuli		= ['click-itd-100us','click-itd-000us','click-itd+100us']
namebody	= '-20hc-fr58_62kHz_unif-20fphc'
# Every stimulus is a list
#	1st item is a stimulus filaname
#	2nd is dictionary with parameters for @STIMULI:prog@ to generate
#		stimulus. Note that for each name first -- will be added, except
#		'stimtype' and inputfile.  4 parameters will be ignored and
#		substituted by parameters by values in configuration: --frequency-range
#		--number-hair-cells, --fiber-per-hcell, --portion-types. Key -s
#		added to command automatically.
#	3rd is tuple of population names and function name, which should be call to
#		load file with stimulus.
#	4th is a name of model output.

click-itd-100us = [
	@STIMULI:stimdir@+'/click-itd-100us'+@STIMULI:namebody@+'.spkl',
	{
		'stimtype'						: 'click',
		'interaural time difference'	: -0.1e-3,
	},
	(('left-input','readfile'),('right-input','readfile')),
	@STIMULI:outputdir@+'/click-itd-100us'+@STIMULI:namebody@+'.pkl'
	]

click-itd-000us = [
	'$STIMULI:stimdir$/click-itd-000us$STIMULI:namebody$.spkl',
	{
		'stimtype'						:'tone',#: 'click',
		'interaural time difference'	: 0.0,
		'stimulus frequency'	: 1500,
	},
	(('left-input','readfile'),('right-input','readfile')),
	'$STIMULI:outputdir$/click-itd-000us$STIMULI:namebody$.pkl'
	]
click-itd+100us = [
	'$STIMULI:stimdir$/click-itd+100us$STIMULI:namebody$.spkl',
	{
		'stimtype'						: 'click',
		'interaural time difference' 	: 0.1e-3,
#		'interaural time difference' 	: 0.1,
	},
	(('left-input','readfile'),('right-input','readfile')),
	'$STIMULI:outputdir$/click-itd+100us$STIMULI:namebody$.pkl'
	]
#an other way to define the stimuli list is make a reference on stimulus objects. 
#However don't forget that option with stimulus MUST exist at this point,
#  so you should move this list down after all stimulus objects.
#stimuli		= [@STIMULI:click-itd-100us@ ,@STIMULI:click-itd-000us@ ,@STIMULI:click-itd+100us@]

[CELLS]
# Cell dictionary with cell-type name and importing command.
CellClasses	= ('AN','VCN','LSO')
AN	:'from spklin import  spklin'
VCN	:'from RaM03  import vcnRaMbase'
LSO	:'from LSO    import LSOcell'
# List of directories which will be scanned for *.mod files
# and mod file will be copy to current directory.
ModsCopy	= @GENERAL:pyextrapath@

[POPULATIONS]
# First item in a list is range of nodes 
#	if it is None: this population is distributed over 
#		all nodes (same as @GENERAL:NODERANGE@[:]),
#	if int. number: it works only on one node with given ID
#	if list or tuple: list of nodes where this cell will be hosted
#	Variable @GENERAL:NODERANGE@ can be used to set list of nodes, for
#   	example @GENERAL:NODERANGE@[4::2] - will run this type of cell
#		on all odd nodes start from the 4. Variables @GENERAL:NODERANGE@ and
#   	@GENERAL:NODEID@ are set by MPI environment.
# Second item is a number of cells in populastion. It may be a lambda function 
#	without arguments for random number of neuron in population, for example.
#
# Third item is a list of subpopulations of cells in population. Should BE THE LIST!
# Every subpopulation is a LIST:
#	Cell type object (see keys in CellImport option if CELLS section)
#	number of gid per one cell - how many gid will be assigned for one cell 
#	Cell marker. It may be a string, int, float. In this case all cell
#	  will have same marker. It maybe a tuple or list by length of number of gid.
#	  in this case each option in list will be mark for individual cell.
#	dictionary of parameters, parameter may be a lambda function
#	function of cell probability - how number of this cell type depends on 
#	  position in population. Function of cell probability should be lambda
#	  from one variable or just a number. Function should return one number 
#	  which is a portion of this cell in population at given position x. 
#	  The position is in a  range[0:1]. Position 0 is a cell with minimal gid
#	  Position 1 is a cell with maximal gid in population
#		EXAMPLE: [ None, 20, ['A', 'A', {}, 1], ['B', 'B', {}, 1] ]
#			Total number of cell in population will be 20 with uniform
#			mixture of cells 'A' and 'B' with 50/50 procent probability
#		EXAMPLE: [ None, 20, ['A', 'A', {}, lambda(x):x], ['B', 'B', {}, lambda(x):1.-x] ]
#			Total number of cell in population will be 20. At beginning of
#			the population (x=0) the probability to find cell 'A' will be
#			zero, cell 'B' one. At the end of population (x=1), the
#			the probability to find cell 'A' is one, and cell 'B' is zero.
#		In both examples cells 'A' has mark 'A', cells 'B' has the mark 'B'
# 
left-input	= [  0, 1,
		[ 'AN',
			@AUDITORY NERVE:nhcell@ * @AUDITORY NERVE:nfibperhcell@,
			[ (f[0],t) for f in @AUDITORY NERVE:anconfig@[0] for t in f[1:] ],
			{
				'anconfig':@AUDITORY NERVE:anconfig@[0],
				'isright':False
			},
			1
		]
	]

right-input	= [  1, 1,
		[ 'AN', 
			@AUDITORY NERVE:nhcell@ * @AUDITORY NERVE:nfibperhcell@,
			[ (f[0],t) for f in @AUDITORY NERVE:anconfig@[1] for t in f[1:] ],
			{
				'anconfig':@AUDITORY NERVE:anconfig@[1],
				'isright':True
			},
			1
		]
	]
#left-sbc = [ None,
left-sbc = [@GENERAL:NODERANGE@[2:], 120,
		[ 'VCN', 1, 'TypeII',
			{
				'Type':'"Type II"'
			},
			lambda x: 1. - x,
		],
		[ 'VCN', 1, 'Type I-c',
			{
				'Type':'"Type I-c"'
			},
			lambda x: x,
		]
	]
right-gbc = [ @GENERAL:NODERANGE@[2:], 120,
		[ 'VCN', 1, 'Type I-c mod',
			{
				'Type':'"Type I-c"',
				'Param':{
					'vcnklt.gkltbar':lambda x: float(1.-x)*1.67e-2,
					'vcnih.ghbar'   :lambda x: float(1.-x)*(1.67e-3-4.2e-5)+4.2e-5
				}
			},
			1
		]
	]
left-lso = [ None, 120,
		[ 'LSO', 1, 'LSO cell',
			{},
			1
		]
	]
[SYNAPSES]
# This section is needed if you have similar synapses between a cells.
# Using this objects from this section will increases computational efficacy.
# Every synapses has an ID and list of parameters. If cell has many of synapses
# with same ID only one module will be inserted in compartment.
# WARNING: all functions in SYNAPSES section will be disabled!
# 	EVERY synapses is a dictionary with one obligatory parameter module.
#     all other names is parameters for this module.
#	  EXAMPLE: {'module':'h.Exp2Syn', 'tua1':0.2, 'tua2':1.0, 'e':0.0}
#		Creates Exp2Syn synapses with tau rising 0.2, tau falling 1.0
#		and reversal potential zero mV
ampa={	'module': 'h.Exp2Syn',
		'tau1':0.1, 'tau2':0.8, 'e':0.0
	}
glyc={	'module': 'h.Exp2Syn',
		'tau1':0.1, 'tau2':1.2, 'e':-70.0
	}

[CONNECTIONS]
# Every connection is a list
#	First and Second items are names of source and destination
#	  populations. If it list it looks like @POPULATIONS:name-of-population@
#	3rd is a boolean. If it's False 5th parameter is a number o connection per source neuron.
#	  If 3rd parameter is True, the 5th is a number a number o connection per destination neuron.
#	4th is boolean. If it True a multiple connections between two cells are allowed.
#	5th is a integer number of connection per source/destination cell. 
#	  It may be an lambda function from two variables. First variable
#	  goes from 0 - minimal gid in population to 1 - maximal gid in population.
#	  If number of connection is positive stochastic algorithm is used, if it's negative
#	  a deterministic algorithm is used for defined connection.
#	  Thw second variable is a neuron marker.
#	  EXAMPLE: lambda x,xm: int(10 * np.sin(np.pi*x) )
#		will set zero connections on the sides and 10 connections per neuron in center.
#	  EXAMPLE: lambda x,xm: np.random.randint(2,high=10)
#		neuron will have random number of connections form 2 to 9 uniformly distributed cross
#		population.
#	  EXAMPLE: lambda x,xm: int(100. + 10. * np.random.randn() )
#		neurons will have 100 connections on average with +-10 SD. 
#	  EXAMPLE: lambda x,xm: int(100. + 10. * np.random.randn() )*int(xm == "Type I-c")
#		Same at in previous example, but this connections work only for Type I-c neurons.
#	6th is a lambda function from 5 variables: offset in source population, mark of a source cell
#	   offset in destination population, mark of cell of destination cell and number os synapse.
#	  It should return boolean value.If returned value is True connection will be made.
#	  EXAMPLE: lambda x,xm,y,ym,z: bool(np.random.rand() < 0.5)
#		Randomly sparsely connected network with 50% probability of connection.
#	  EXAMPLE: lambda x,xm,y,ym: bool(np.random.rand() < np.exp(-(x-y)**2/0.2**2) )
#		Probability to get two cells connected decreases by normal low with distance
#	  EXAMPLE: lambda x,xm,y,ym,z: bool(np.random.rand() < np.exp(-(x-y)**2/0.2**2) ) and xm[1] == 1 and ym = "Type I-c"
#		Same as previous example but only connect specific subpopulations of cells.
#	7th is maximal synaptic conductance in uS It may be a lambda function same from 5
#	  parameters or just an number.
#	  EXAMPLE: 0.003
#		Synaptic conductance is constant
#	  EXAMPLE: lambda x,xm,y,ym,z:  np.exp(-(x-y)**2/0.2**2)*0.02
#		Synaptic weight decrease with distance between two neurons
#	8th is a delay. Delay may be an float number or lambda function from
#	  5 variables.
#	  EXAMPLE: 0.3
#		0.3 ms delay
#	  EXAMPLE: lambda x,xm,y,ym,z: np.abs(x-y)*2.5+0.1
#		Delay increases with distance between neurons. Min delay 0.1
#	9th is a synapses position. position is a tuple. First is compartment
#	  name second is float from 0 to 1 - compartment offset
#	  WARNING! You can easily overload memory if you will use float random number
#	  for compartment offset. To avoid this use this idiom
#	  float(np.random.randint(number_of_segments_in_compartment)/number_of_segments_in_compartment
#	10th name of synapse in SYNAPSES section or synaptic parameters (same as synapses description).
#	  Please note that is you would like to vary synaptic parameters
#	  you should specify synapses here directly.

left-input2left-sbc=['left-input','left-sbc',
	True, False,
	lambda x,xm: np.random.randint(2,high=5),
	lambda x,xm,y,ym,z: bool( np.random.rand() < np.exp(-(x-y)**2/0.4**2) ),
	3e-3,
	lambda x,xm,y,ym,z: np.abs(x-y)*0.5+0.1,
	('soma',0.5),
	'ampa'
	]

right-input2right-gbc=['right-input','right-gbc',
	False,False, 
	lambda x,xm: np.random.randint(10,high=50),
	lambda x,xm,y,ym,z: bool( np.random.rand() < np.exp(-(x-y)**2/0.2**2) ),
	5e-3,
	lambda x,xm,y,ym,z: np.abs(x-y)*0.5+0.1,
	('soma',0.5),
	'ampa'
	]
left-sbc2left-lso=['left-sbc','left-lso',
	True,False,
	lambda x,xm: np.random.randint(5,high=11),
	lambda x,xm,y,ym,z: bool( np.random.rand() < np.exp(-(x-y)**2/0.4**2) ),
	6e-3,
	lambda x,xm,y,ym,z: np.abs(x-y)*0.5+0.1,
	([ 'dends[0]', 'dends[1]'][np.random.randint(2)],lambda x,xm,y,ym,z:float(np.random.randint(10))/10.),
	'ampa'
	]
right-gbc2left-lso=['right-gbc','left-lso',
	True,False,
	lambda x,xm: np.random.randint(1,high=4),
	lambda x,xm,y,ym,z: bool( np.random.rand() < np.exp(-(x-y)**2/0.4**2) ),
	1e-3,
	lambda x,xm,y,ym,z: np.abs(x-y)*0.5+0.1,
	('soma',0.5),
	'glyc'
	]
	
[RECORD]
# Record section define additional variables which will be recorded during
# simulation. 
# To record cells add recorders to cells option
cells		= ['left-sbc','right-gbc','right-gbc-ih','left-lso-soma', 'left-lso-axon']
# Recorder for cells is a list.
#	1st parameter is population name or link to population
#	2nd int, tuple, list or None. If 2nd parameter is :
#       int   - record cell with id inside the population
#		list  is a set of ids
#		tuple is start id, stop id and step
#		None  - record all cells within population
#	3rd compartment name
#	4th compartment offset
#	5th module name or None
#	6th is recorded variable name
left-sbc	= [ 'left-sbc', (0,None,10),'soma',0.5,None,'v' ]
right-gbc-ih= [ 'right-gbc', (0,None,10),'soma',0.5,'vcnih','i']
right-gbc	= [ @POPULATIONS:right-gbc@, [0,@POPULATIONS:right-gbc@[1]/4,@POPULATIONS:right-gbc@[1]/2,@POPULATIONS:right-gbc@[1]*3/4,@POPULATIONS:right-gbc@[1]-1],'soma',0.5,None,'v' ]
left-lso-soma	= [ 'left-lso', (0,None,10),'soma',0.5,None,'v' ]
left-lso-axon	= [ 'left-lso', (0,None,10),'axon',0.5,None,'v' ]
##### DOESN'T WORK YET!!!! #########
# To synaptic activity add recorders to synapses option.
#synapses	= ['left-input2left-sbc','right-input2right-gbc']

[SIMULATION]
#threads option is ignored ignored if MPI parallelization is active.
#if MPI clustering is disabled script will run multithread parallelization.
#To disable threading set threads at zero or any negative number or call script
#with --no-threads flag. Flag will alter this option.
threads = 4

#all options started with h. will by set in NEURON
h.dt = 0.01
h.celsius = 22.

#h.tstop option sets the time when simulation is terminanted
#regardless of stimulus duration. If h.tstop isn't set the dulation
#of simulation depends on duration of each stimulus. (in ms)
#h.tstop = 300

[GRAPHS]
import	= [
	'import matplotlib.pyplot as plt'
	]
Figures = ['VCN-LSO-traces','AN-VCN-LSO-rasters','show']
#, 'Figure-2', 'Figure-3']
# Filename
VCN-LSO-traces = [
	'plt.figure(1,figsize=(18,8))',
	'p1=plt.subplot(5,3,1)',
	'plot_stimwave(\'click-itd-100us\',False)',#left stimulus waveform
	'plot_stimwave(\'click-itd-100us\',True)', #right stimulus waveform
	'plt.ylabel(\'left/right wave\')',
	'p2=plt.subplot(5,3,2)',
	'plot_stimwave(\'click-itd-000us\',False)',#left stimulus waveform
	'plot_stimwave(\'click-itd-000us\',True)', #right stimulus waveform
	'p3=plt.subplot(5,3,3)',
	'plot_stimwave(\'click-itd+100us\',False)',#left stimulus waveform
	'plot_stimwave(\'click-itd+100us\',True)', #right stimulus waveform
#Second row all recorded cell left-sbc
	'plt.subplot(5,3,4,sharex=p1)',
	'plot_traces(\'click-itd-100us\',\'left-sbc\')',
	'plt.ylabel(\'SBS voltage(mV)\')',
	'plt.subplot(5,3,5,sharex=p2)',
	'plot_traces(\'click-itd-000us\',\'left-sbc\')',
	'plt.subplot(5,3,6,sharex=p3)',
	'plot_traces(\'click-itd+100us\',\'right-gbc\')',
#Third row all rec for right-gbc
	'plt.subplot(5,3,7,sharex=p1)',
	'plot_traces(\'click-itd-100us\',\'right-gbc\')',
	'plt.ylabel(\'GBS voltage (mV)\')',
	'plt.subplot(5,3,8,sharex=p2)',
	'plot_traces(\'click-itd-000us\',\'right-gbc\')',
	'plt.subplot(5,3,9,sharex=p3)',
	'plot_traces(\'click-itd+100us\',\'right-gbc\')',
#4th row all rec for left-lso-soma
	'plt.subplot(5,3,10,sharex=p1)',
	'plot_traces(\'click-itd-100us\',\'left-lso-soma\')',
	'plt.ylabel(\'LSO soma voltage (mV)\')',
	'plt.subplot(5,3,11,sharex=p2)',
	'plot_traces(\'click-itd-000us\',\'left-lso-soma\')',
	'plt.subplot(5,3,12,sharex=p3)',
	'plot_traces(\'click-itd+100us\',\'left-lso-soma\')',
#5th row all rec for left-lso-axon
	'plt.subplot(5,3,13,sharex=p1)',
	'plot_traces(\'click-itd-100us\',\'left-lso-axon\')',
	'plt.ylabel(\'LSO axon voltage (mV)\')',
	'plt.subplot(5,3,14,sharex=p2)',
	'plot_traces(\'click-itd-000us\',\'left-lso-axon\')',
	'plt.subplot(5,3,15,sharex=p3)',
	'plot_traces(\'click-itd+100us\',\'left-lso-axon\')',
	]

AN-VCN-LSO-rasters = [
	'plt.figure(2,figsize=(18,8))',
	'p1=plt.subplot(5,3,1)',
	'plot_stimwave(\'click-itd-100us\',False)',#left stimulus waveform
	'plot_stimwave(\'click-itd-100us\',True)', #right stimulus waveform
	'p2=plt.subplot(5,3,2)',
	'plot_stimwave(\'click-itd-000us\',False)',#left stimulus waveform
	'plot_stimwave(\'click-itd-000us\',True)', #right stimulus waveform
	'p3=plt.subplot(5,3,3)',
	'plot_stimwave(\'click-itd+100us\',False)',#left stimulus waveform
	'plot_stimwave(\'click-itd+100us\',True)', #right stimulus waveform
#2nd row all rasters of AN
	'plt.subplot(5,3,4,sharex=p1)',
	'plot_raster(\'click-itd-100us\',[\'left-input\',\'right-input\'])',
	'plt.ylabel(\'Left and right input\')',
	'plt.subplot(5,3,5,sharex=p2)',
	'plot_raster(\'click-itd-000us\',[\'left-input\',\'right-input\'])',
	'plt.subplot(5,3,6,sharex=p3)',
	'plot_raster(\'click-itd+100us\',[\'left-input\',\'right-input\'])',
#Third row all rasters cell left-sbc
	'plt.subplot(5,3,7,sharex=p1)',
	'plot_raster(\'click-itd-100us\',\'left-sbc\')',
	'plt.ylabel(\'SBS raster\')',
	'plt.subplot(5,3,8,sharex=p2)',
	'plot_raster(\'click-itd-000us\',\'left-sbc\')',
	'plt.subplot(5,3,9,sharex=p3)',
	'plot_raster(\'click-itd+100us\',\'right-gbc\')',
#4th row rasters for right-gbc
	'plt.subplot(5,3,10,sharex=p1)',
	'plot_raster(\'click-itd-100us\',\'right-gbc\')',
	'plt.ylabel(\'GBS raster\')',
	'plt.subplot(5,3,11,sharex=p2)',
	'plot_raster(\'click-itd-000us\',\'right-gbc\')',
	'plt.subplot(5,3,12,sharex=p3)',
	'plot_raster(\'click-itd+100us\',\'right-gbc\')',
#5th row all rasters for left-lso
	'plt.subplot(5,3,13,sharex=p1)',
	'plot_raster(\'click-itd-100us\',\'left-lso\')',
	'plt.ylabel(\'LSO raster\')',
	'plt.subplot(5,3,14,sharex=p2)',
	'plot_raster(\'click-itd-000us\',\'left-lso\')',
	'plt.subplot(5,3,15,sharex=p3)',
	'plot_raster(\'click-itd+100us\',\'left-lso\')',
	]
show = [ 'plt.show()' ]
[VIEW]

[STAT]
#Stat section allows collect statistics
# Results will be save in coma separated value (CSV) file.
# CSV file name defined by file option
file		= 'stat.csv'
# The delimiter in CVS may be altered by delimiter option
# You can make tcv or dat file separated by space
delimiter	= ','
# Every row in the file is a result of one simulation.
# Each column in a row defines a test.
# records option defines all options which will be collected for each simulation.
records 	= ['total-click-itd-100us left-lso','total-click-itd+100us right-gbc', 'total-topne-itd-000us both']

total-click-itd-100us left-lso  = 'stat_total(\'click-itd-100us\',\'left-lso\')'
total-click-itd+100us right-gbc = 'stat_total(\'click-itd+100us\',\'right-gbc\')'
total-topne-itd-000us both      = 'stat_total(\'click-itd-000us\',[\'left-lso\',\'right-gbc\'])'

[SCRIPTS]
# You can run arbitrary script at different stages of simulation process.
# preloading, preparation, simulations, analysis are option which are read
#  and executed in a different stage of model preparation and simulations running.
# Each section is a list of system commands which should be run.

#Extra command should be defined first
git commit = "git commit -a &"

#scripts which are run before any manipulations
preloading  = []
#scripts which are run before preset
preparation = []
#scripts which are run before simulation
simulations = []
#scripts which are run before any analysis
analysis    = [ @SCRIPTS:git commit@ ]