Merge pull request #211 from Neurosim-lab/development

PR from development to master - VERSION 0.6.9

CHANGES.md

@@ -1,3 +1,43 @@
+# Version 0.6.9
+
+- Improved support for NeuroML export
+
+- Added option to skip batch job based on custom existing job filename (eg. 'skipCustom': '.run') 
+
+- Added option to specify netParams .py file for batch 
+
+- Modified hpc_torque batch to accept nodes and ppn
+
+- New option to import cell with sections not inside an object 
+
+- plotShape now shows real diameters in interviews by default
+
+- Added option to set threshold when loading weightNorm
+
+- Added support for conditions on recordTraces
+
+- Fixed bug plotting NetStims (created as stims) in raster, spikeHist and ratePSD  
+
+- Fixed bug in plotConn bar graphs
+
+- Fixed bug: missing hRandom for NetStim populations
+
+- Fixed bug: use Random123() instead of netstim.noiseFromRandom for NetStim stims
+
+- Fixed bug: VectStim spike generation now reproducible for different durations
+
+- Fixed bug in batch grouped params
+
+- Fixed bug: getCellsList() only checked for tuples but when load from json converted to list
+
+- Fixed bug so cell treshold is set by default to sim.net.defaultThreshold
+
+- Fixed bug plotting overlayed spikeHist over raster plot
+
+- Fixed bug in plotShape when includeAxon=False
+
+
+
 # Version 0.6.8
 
 - Keep track of last host after distributing cells of each pop (improves load balance) (issues #41 #196)

examples/HHTut/HHTut.py

@@ -25,12 +25,20 @@
 ###############################################################################
 
 # Population parameters
-netParams.popParams['PYR'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 100} # add dict with params for this pop 
+netParams.popParams['PYR'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 100, 'yRange': [300, 500], 'color': 'red'} # add dict with params for this pop 
+netParams.popParams['PYR'] = {'cellModel': 'NetStim', 'cellType': 'PYR2', 'numCells': 100} # add dict with params for this pop 
 
 
 # Cell parameters
 ## PYR cell properties
-cellRule = {'conds': {'cellModel': 'HH', 'cellType': 'PYR'},  'secs': {}} 	# cell rule dict
+cellRule = {'conds': {'cellModel': 'HH', 'cellType': 'PYT'},  'secs': {}} 	# cell rule dict
+cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}  														# soma params dict
+cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}  									# soma geometry
+cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}  		# soma hh mechanism
+cellRule['secs']['soma']['vinit'] = -71
+netParams.cellParams['PYR'] = cellRule  												# add dict to list of cell params
+
+cellRule = {'conds': {'color': 'red'},  'secs': {}} 	# cell rule dict
 cellRule['secs']['soma'] = {'geom': {}, 'mechs': {}}  														# soma params dict
 cellRule['secs']['soma']['geom'] = {'diam': 18.8, 'L': 18.8, 'Ra': 123.0}  									# soma geometry
 cellRule['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70}  		# soma hh mechanism

examples/NeuroMLImport/poissonFiringSyn.mod

@@ -26,7 +26,9 @@ NEURON {
     RANGE syn0_g                            : exposure
 
     RANGE syn0_i                            : exposure
-
+    : Based on netstim.mod
+    THREADSAFE : only true if every instance has its own distinct Random
+    POINTER donotuse
 }
 
 UNITS {
@@ -70,7 +72,7 @@ ASSIGNED {
     rate_tsince (ms/ms)
     rate_syn0_A (/ms)
     rate_syn0_B (/ms)
-
+    donotuse
 }
 
 STATE {
@@ -164,10 +166,67 @@ PROCEDURE rates() {
 }
 
 
-: Returns a float between 0 and max
+: Returns a float between 0 and max; implementation of random() as used in LEMS
 FUNCTION random_float(max) {
 
-    random_float = scop_random()*max
+    : This is not ideal, getting an exponential dist random number and then turning back to uniform
+    : However this is the easiest what to ensure mod files with random methods fit into NEURON's
+    : internal framework for managing internal number generation.
+    random_float = exp(-1*erand())*max
 
 }
 
+:****************************************************
+: Methods copied from netstim.mod in NEURON source
+
+
+PROCEDURE seed(x) {
+	set_seed(x)
+}
+
+VERBATIM
+double nrn_random_pick(void* r);
+void* nrn_random_arg(int argpos);
+ENDVERBATIM
+
+
+FUNCTION erand() {
+VERBATIM
+	if (_p_donotuse) {
+		/*
+		:Supports separate independent but reproducible streams for
+		: each instance. However, the corresponding hoc Random
+		: distribution MUST be set to Random.negexp(1)
+		*/
+		_lerand = nrn_random_pick(_p_donotuse);
+	}else{
+		/* only can be used in main thread */
+		if (_nt != nrn_threads) {
+           hoc_execerror("multithread random in NetStim"," only via hoc Random");
+		}
+ENDVERBATIM
+		: the old standby. Cannot use if reproducible parallel sim
+		: independent of nhost or which host this instance is on
+		: is desired, since each instance on this cpu draws from
+		: the same stream
+		erand = exprand(1)
+VERBATIM
+	}
+ENDVERBATIM
+}
+
+PROCEDURE noiseFromRandom() {
+VERBATIM
+ {
+	void** pv = (void**)(&_p_donotuse);
+	if (ifarg(1)) {
+		*pv = nrn_random_arg(1);
+	}else{
+		*pv = (void*)0;
+	}
+ }
+ENDVERBATIM
+}
+
+: End of methods copied from netstim.mod in NEURON source:****************************************************
+

examples/sandbox/sandbox.py

@@ -27,6 +27,27 @@
 simConfig = specs.SimConfig()  # dictionary to store sets of simulation configurations
 
 
+###############################################################################
+# SIMULATION PARAMETERS
+###############################################################################
+
+# Simulation parameters
+simConfig.duration = 1.0*1e3 # Duration of the simulation, in ms
+simConfig.dt = 0.1 # Internal integration timestep to use
+simConfig.createNEURONObj = 1  # create HOC objects when instantiating network
+simConfig.createPyStruct = 1  # create Python structure (simulator-independent) when instantiating network
+simConfig.verbose = 0 #False  # show detailed messages 
+
+# Recording 
+simConfig.recordCells = [('PYR1',5), 8]
+simConfig.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}}
+
+# # Analysis and plotting 
+#simConfig.analysis['plotRaster'] = {'figSize': (20,8)} #True
+#simConfig.analysis['plot2Dnet'] = True
+
+
+
 ###############################################################################
 # NETWORK PARAMETERS
 ###############################################################################
@@ -38,9 +59,10 @@
 
 
 # Population parameters
-netParams.popParams['PYR1'] = {'cellModel': 'HH', 'cellType': 'PYR', 'gridSpacing': 10, 'xRange': [30,60]} # pop of HH cells
-netParams.popParams['PYR2'] = {'cellModel': 'HH', 'cellType': 'PYR', 'gridSpacing': 5, 'yRange': [20,40]} # pop of HH cells
-#netParams.popParams['artifVec'] = {'cellModel': 'VecStim', 'numCells': 10000, 'interval': 100, 'noise': 0.5, 'start': 50}  # pop of NetStims
+netParams.popParams['PYR1'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 100} # 'gridSpacing': 10, 'xRange': [30,60]} # pop of HH cells
+netParams.popParams['PYR2'] = {'cellModel': 'HH', 'cellType': 'PYR', 'numCells': 100} # 'gridSpacing': 5, 'yRange': [20,40]} # pop of HH cells
+#netParams.popParams['artifVec'] = {'cellModel': 'VecStim', 'numCells': 1, 'interval': 100, 'noise': 0.5, 'start': 50, 
+#    'pulses':[{'start': 1000, 'end': 1400, 'rate': 100, 'noise': 0.5}]}  # pop of NetStims
 # netParams.popParams['artif1'] = {'cellModel': 'VecStim', 'numCells': 100, 'rate': [0,5], 'noise': 1.0, 'start': 50}#, 
 #    'pulses': [{'start': 200, 'end': 300, 'rate': 50, 'noise':0.2}, {'start': 500, 'end': 800, 'rate': 30, 'noise':0.5}]}  # pop of NetStims
 
@@ -49,8 +71,8 @@
 
 
 # Stimulation parameters
-# netParams.stimSourceParams['background'] = {'type': 'NetStim', 'interval': 100, 'number': 1e5, 'start': 500, 'noise': 0.5}  # stim using NetStims after 500ms
-# netParams.stimTargetParams['bkg->PYR1'] = {'source': 'background', 'conds': {'popLabel': 'PYR1'}, 'sec':'soma', 'loc': 0.5, 'weight': 0.5, 'delay': 1}
+netParams.stimSourceParams['background'] = {'type': 'NetStim', 'interval': 20, 'number': 1e5, 'start': 500, 'noise': 0.5}  # stim using NetStims after 500ms
+netParams.stimTargetParams['bkg->PYR1'] = {'source': 'background', 'conds': {'popLabel': 'PYR1'}, 'sec':'soma', 'loc': 0.5, 'weight': 0.5, 'delay': 1}
 
 
 # Cell parameters
@@ -63,53 +85,46 @@
 
 
 # Connections
-netParams.connParams['artif1->PYR1'] = {
-    'preConds': {'popLabel': 'artif1'}, 'postConds': {'popLabel': 'PYR1'},
-    'convergence': 4,
-    'weight': 0.005,                    
-    'synMech': 'AMPA',                
-    'delay': 'uniform(1,5)',
-    'synsPerConn': 1}          
-
-netParams.connParams['PYR2->PYR1'] = {
-    'preConds': {'popLabel': 'PYR2'}, 'postConds': {'popLabel': 'PYR1'},
+# netParams.connParams['artif1->PYR1'] = {
+#     'preConds': {'popLabel': 'artif1'}, 'postConds': {'popLabel': 'PYR1'},
+#     'convergence': 4,
+#     'weight': 0.005,                    
+#     'synMech': 'AMPA',                
+#     'delay': 'uniform(1,5)',
+#     'synsPerConn': 1}          
+
+netParams.connParams['PYR1->PYR2_1'] = {
+    'preConds': {'popLabel': 'PYR1'}, 'postConds': {'popLabel': 'PYR2'},
     'probability': 0.1,
     'weight': 0.2,                     
     'delay': 'uniform(1,5)',
     'synsPerConn': 1}     
 
-netParams.addConnParams('artif1->PYR2',
-    {'preConds': {'popLabel': 'artif1'}, 'postConds': {'popLabel': 'PYR2'}, 
-    'divergence': 3,
-    'weight': 0.05,              
-    'delay': 3,
-    'synsPerConn': 1})        
-
-
-###############################################################################
-# SIMULATION PARAMETERS
-###############################################################################
-
-# Simulation parameters
-simConfig.duration = 0.1*1e3 # Duration of the simulation, in ms
-simConfig.dt = 0.1 # Internal integration timestep to use
-simConfig.createNEURONObj = 1  # create HOC objects when instantiating network
-simConfig.createPyStruct = 1  # create Python structure (simulator-independent) when instantiating network
-simConfig.verbose = 0 #False  # show detailed messages 
+netParams.connParams['PYR1->PYR2_2'] = {
+    'preConds': {'popLabel': 'PYR1'}, 'postConds': {'popLabel': 'PYR2'},
+    'probability': 0.1,
+    'weight': 0.4,                     
+    'delay': 'uniform(1,5)',
+    'synsPerConn': 1} 
 
-# Recording 
-simConfig.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}}
+# netParams.addConnParams('artif1->PYR2',
+#     {'preConds': {'popLabel': 'artif1'}, 'postConds': {'popLabel': 'PYR2'}, 
+#     'divergence': 3,
+#     'weight': 0.05,              
+#     'delay': 3,
+#     'synsPerConn': 1})        
 
-# # Analysis and plotting 
-simConfig.analysis['plotRaster'] = True
-simConfig.analysis['plot2Dnet'] = True
 
 
 ###############################################################################
 # RUN SIM
 ###############################################################################
+simConfig.analysis['plotRaster'] = {'saveFig': True, 'showFig':True, 'labels': 'overlay', 'popRates': True, 
+                                    'orderInverse': True, 'figSize': (12,10), 'lw': 0.6, 'marker': '|'} 
 
 sim.createSimulateAnalyze()
+
+
 #sim.create()
 #sim.gatherData()
 

netpyne/__init__.py

@@ -1,3 +1,3 @@
 
-__version__ = '0.6.8' 
+__version__ = '0.6.9' 
 __gui__ = True  # global option to enable/disable graphics