Changeset 713

Timestamp:

02/17/10 22:45:18 (2 years ago)

Author:

apdavison

Message:

Moved standard model (cells and synapses) machinery out of the common module into its own module

Location:

trunk/src

Files:

• brian/__init__.py (modified) (3 diffs)
• brian/cells.py (modified) (10 diffs)
• brian/simulator.py (modified) (6 diffs)
• brian/synapses.py (modified) (3 diffs)
• cells.py (modified) (1 diff)
• common.py (modified) (17 diffs)
• connectors.py (modified) (2 diffs)
• connectors2.py (modified) (4 diffs)
• core.py (added)
• nest/__init__.py (modified) (3 diffs)
• nest/cells.py (modified) (12 diffs)
• nest/simulator.py (modified) (6 diffs)
• nest/synapses.py (modified) (8 diffs)
• neuron/__init__.py (modified) (3 diffs)
• neuron/cells.py (modified) (10 diffs)
• neuron/simulator.py (modified) (5 diffs)
• neuron/synapses.py (modified) (5 diffs)
• pcsim/__init__.py (modified) (7 diffs)
• pcsim/cells.py (modified) (12 diffs)
• pcsim/simulator.py (modified) (5 diffs)
• pcsim/synapses.py (modified) (9 diffs)
• standardmodels.py (added)
• synapses.py (modified) (1 diff)

trunk/src/brian/__init__.py

@@ -9 +9 @@
 #import brian_no_units_no_warnings
 from pyNN.brian import simulator
-from pyNN import common, recording, space, __doc__
+from pyNN import common, recording, space, standardmodels, __doc__
 common.simulator = simulator
 recording.simulator = simulator
@@ -23 +23 @@
 def list_standard_models():
     """Return a list of all the StandardCellType classes available for this simulator."""
-    standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, common.StandardCellType)]
+    standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, standardmodels.StandardCellType)]
     for cell_class in standard_cell_types:
         try:
@@ -102 +102 @@
           e.g., (10,10) will create a two-dimensional population of size 10x10.
         cellclass should either be a standardized cell class (a class inheriting
-        from common.StandardCellType) or a string giving the name of the
+        from standardmodels.StandardCellType) or a string giving the name of the
         simulator-specific model that makes up the population.
         cellparams should be a dict which is passed to the neuron model

trunk/src/brian/cells.py

@@ -5 +5 @@
 """
 
-from pyNN import common, cells, errors
+from pyNN import standardmodels, cells, errors
 #import brian_no_units_no_warnings
 from brian.library.synapses import *
@@ -16 +16 @@
     """Leaky integrate and fire model with fixed threshold and alpha-function-
     shaped post-synaptic current."""
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'v_rest',     mV),
         ('v_reset',    'v_reset'),
@@ -51 +51 @@
     excitatory and inhibitory synapses."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'v_rest',     mV),
         ('v_reset',    'v_reset'),
@@ -81 +81 @@
 
 class IF_cond_alpha(cells.IF_cond_alpha):
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'v_rest',     mV),
         ('v_reset',    'v_reset'),
@@ -117 +117 @@
     """Leaky integrate and fire model with fixed threshold and 
     exponentially-decaying post-synaptic conductance."""
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'v_rest',     mV),
         ('v_reset',    'v_reset'),
@@ -149 +149 @@
 
 
-class IF_facets_hardware1(common.ModelNotAvailable):
+class IF_facets_hardware1(standardmodels.ModelNotAvailable):
     """Leaky integrate and fire model with conductance-based synapses and fixed
     threshold as it is resembled by the FACETS Hardware Stage 1. For further
@@ -160 +160 @@
 class SpikeSourcePoisson(cells.SpikeSourcePoisson):
     """Spike source, generating spikes according to a Poisson process."""
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('rate',     'rate'),
         ('start',    'start'),
@@ -189 +189 @@
 class SpikeSourceArray(cells.SpikeSourceArray):
     """Spike source generating spikes at the times given in the spike_times array."""
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('spike_times', 'spiketimes', ms),
     )
@@ -202 +202 @@
         return super(SpikeSourceArray, cls).translate(parameters)
 
-class EIF_cond_alpha_isfa_ista(common.ModelNotAvailable):
-    pass
-
-
-class HH_cond_exp(common.ModelNotAvailable):
+class EIF_cond_alpha_isfa_ista(standardmodels.ModelNotAvailable):
+    pass
+
+
+class HH_cond_exp(standardmodels.ModelNotAvailable):
     pass
     
 #class HH_cond_exp(cells.HH_cond_exp):
 #    
-#    translations = common.build_translations(
+#    translations = standardmodels.build_translations(
 #        ('gbar_Na',    'gbar_Na'),   
 #        ('gbar_K',     'gbar_K'),    
@@ -259 +259 @@
 
 
-class SpikeSourceInhGamma(common.ModelNotAvailable):
-    pass
-
-
-class IF_cond_exp_gsfa_grr(common.ModelNotAvailable):
-    pass
+class SpikeSourceInhGamma(standardmodels.ModelNotAvailable):
+    pass
+
+
+class IF_cond_exp_gsfa_grr(standardmodels.ModelNotAvailable):
+    pass

trunk/src/brian/simulator.py

@@ -32 +32 @@
 from itertools import izip
 import scipy.sparse
-from pyNN import common, cells, errors
+from pyNN import common, cells, errors, standardmodels, core
 
 mV = brian.mV
@@ -143 +143 @@
         return self._threshold.spiketimes
     def _set_spiketimes(self, spiketimes):
-        assert common.is_listlike(spiketimes)
-        if len(spiketimes) == 0 or common.is_number(spiketimes[0]):
+        assert core.is_listlike(spiketimes)
+        if len(spiketimes) == 0 or numpy.isscalar(spiketimes[0]):
             spiketimes = [spiketimes for i in xrange(len(self))]
         assert len(spiketimes) == len(self), "spiketimes (length %d) must contain as many iterables as there are cells in the group (%d)." % (len(spiketimes), len(self))
@@ -279 +279 @@
                                         max_delay=state.max_delay)
         cell_parameters = cellparams or {}
-    elif isinstance(cellclass, type) and issubclass(cellclass, common.StandardCellType):
+    elif isinstance(cellclass, type) and issubclass(cellclass, standardmodels.StandardCellType):
         celltype = cellclass(cellparams)
         cell_parameters = celltype.parameters
@@ -469 +469 @@
         """
         #print "connecting", source, "to", targets, "with weights", weights, "and delays", delays
-        if not common.is_listlike(targets):
+        if not core.is_listlike(targets):
             targets = [targets]
         if isinstance(weights, float):
@@ -585 +585 @@
         else:
             raise Exception("Setting parameters other than weight and delay not yet supported.")
-        if common.is_number(value):
+        if numpy.isscalar(value):
             for row in M.data:
                 for i in range(len(row)):
@@ -593 +593 @@
             for (i,j) in address_gen:
                 M[i,j] = value[i,j]*units
-        elif common.is_listlike(value):
+        elif core.is_listlike(value):
             assert len(value) == M.getnnz()
             address_gen = ((i,j) for i,row in enumerate(bc.W.rows) for j in row)

trunk/src/brian/synapses.py

@@ -5 +5 @@
 """
 
-from pyNN import common
+from pyNN import standardmodels
 
 
-class SynapseDynamics(common.SynapseDynamics):
+class SynapseDynamics(standardmodels.SynapseDynamics):
     def __init__(self, fast=None, slow=None):
         synapses.SynapseDynamics.__init__(self, fast, slow)
 
-class STDPMechanism(common.STDPMechanism):
+class STDPMechanism(standardmodels.STDPMechanism):
     def __init__(self, timing_dependence=None, weight_dependence=None,
                  voltage_dependence=None, dendritic_delay_fraction=1.0):
@@ -21 +21 @@
                                       voltage_dependence, dendritic_delay_fraction)
 
-class TsodkysMarkramMechanism(common.ModelNotAvailable):
+class TsodkysMarkramMechanism(standardmodels.ModelNotAvailable):
     
     def __init__(self, U=0.5, tau_rec=100.0, tau_facil=0.0, u0=0.0, x0=1.0, y0=0.0):
@@ -35 +35 @@
         population.v_[spikes]= v_reset
 
-class AdditiveWeightDependence(common.ModelNotAvailable):
+class AdditiveWeightDependence(standardmodels.ModelNotAvailable):
     pass
 
-class MultiplicativeWeightDependence(common.ModelNotAvailable):
+class MultiplicativeWeightDependence(standardmodels.ModelNotAvailable):
     pass
 
-class SpikePairRule(common.ModelNotAvailable):
+class SpikePairRule(standardmodels.ModelNotAvailable):
     pass

trunk/src/cells.py

@@ -28 +28 @@
 
 import numpy
-from common import StandardCellType
+from standardmodels import StandardCellType
 
 class IF_curr_alpha(StandardCellType):

trunk/src/common.py

@@ -9 +9 @@
 
 Utility functions and classes:
-    is_listlike()
-    is_number()
     is_conductance()
     check_weight()
     check_delay()
-    distance()
     
 Accessing individual neurons:
     IDMixin
-
-Standard cells/parameter translation:
-    build_translations()    
-    StandardModelType
-    StandardCellType
-    ModelNotAvailable
     
 Common API implementation/base classes:
@@ -46 +37 @@
     Population
     Projection
-    
-  4. Specification of synaptic plasticity:
-    SynapseDynamics
-    ShortTermPlasticityMechanism
-    STDPMechanism
-    STDPWeightDependence
-    STDPTimingDependence
 
 $Id$
 """
 
-import types, copy, sys
 import numpy
 import logging
 from math import *
 import operator
-from pyNN import random, utility, recording, errors
+from pyNN import random, utility, recording, errors, standardmodels, core
 from string import Template
 
@@ -79 +62 @@
 # ==============================================================================
 
-def is_listlike(obj):
-    return hasattr(obj, "__len__") and not isinstance(obj, basestring)
-
-def is_number(obj):
-    return isinstance(obj, float) or isinstance(obj, int) or isinstance(obj, long) or isinstance(obj, numpy.float64)
 
 def is_conductance(target_cell):
@@ -103 +81 @@
     if weight is None:
         weight = DEFAULT_WEIGHT
-    if is_listlike(weight):
+    if core.is_listlike(weight):
         weight = numpy.array(weight)
         nan_filter = (1-numpy.isnan(weight)).astype(bool) # weight arrays may contain NaN, which should be ignored
@@ -111 +89 @@
         if not (all_negative or all_positive):
             raise errors.InvalidWeightError("Weights must be either all positive or all negative")
-    elif is_number(weight):
+    elif numpy.isscalar(weight):
         all_positive = weight >= 0
         all_negative = weight < 0
@@ -224 +202 @@
     
     def is_standard_cell(self):
-        return (type(self.cellclass) == type and issubclass(self.cellclass, StandardCellType))
+        return (type(self.cellclass) == type and issubclass(self.cellclass, standardmodels.StandardCellType))
         
     def _set_position(self, pos):
@@ -267 +245 @@
         """Inject current from a current source object into the cell."""
         current_source.inject_into([self])
-        
-
-# ==============================================================================
-#   Standard cells
-# ==============================================================================
-
-def build_translations(*translation_list):
-    """
-    Build a translation dictionary from a list of translations/transformations.
-    """
-    translations = {}
-    for item in translation_list:
-        assert 2 <= len(item) <= 4, "Translation tuples must have between 2 and 4 items. Actual content: %s" % str(item)
-        pynn_name = item[0]
-        sim_name = item[1]
-        if len(item) == 2: # no transformation
-            f = pynn_name
-            g = sim_name
-        elif len(item) == 3: # simple multiplicative factor
-            scale_factor = item[2]
-            f = "float(%g)*%s" % (scale_factor, pynn_name)
-            g = "%s/float(%g)" % (sim_name, scale_factor)
-        elif len(item) == 4: # more complex transformation
-            f = item[2]
-            g = item[3]
-        translations[pynn_name] = {'translated_name': sim_name,
-                                   'forward_transform': f,
-                                   'reverse_transform': g}
-    return translations
-
-class StandardModelType(object):
-    """Base class for standardized cell model and synapse model classes."""
-    
-    translations = {}
-    default_parameters = {}
-    
-    def __init__(self, parameters):
-        self.parameters = self.__class__.checkParameters(parameters, with_defaults=True)
-        self.parameters = self.__class__.translate(self.parameters)
-    
-    @classmethod
-    def checkParameters(cls, supplied_parameters, with_defaults=False):
-        """
-        Returns a parameter dictionary, checking that each
-        supplied_parameter is in the default_parameters and
-        converts to the type of the latter.
-
-        If with_defaults==True, parameters not in
-        supplied_parameters are in the returned dictionary
-        as in default_parameters.
-
-        """
-        default_parameters = cls.default_parameters
-        if with_defaults:
-            parameters = copy.copy(default_parameters)
-        else:
-            parameters = {}
-        if supplied_parameters:
-            for k in supplied_parameters.keys():
-                if default_parameters.has_key(k):
-                    err_msg = "For %s in %s, expected %s, got %s (%s)" % \
-                              (k, cls.__name__, type(default_parameters[k]),
-                               type(supplied_parameters[k]), supplied_parameters[k])
-                    # same type
-                    if type(supplied_parameters[k]) == type(default_parameters[k]): 
-                        parameters[k] = supplied_parameters[k]
-                    # float and something that can be converted to a float
-                    elif type(default_parameters[k]) == types.FloatType: 
-                        try:
-                            parameters[k] = float(supplied_parameters[k]) 
-                        except (ValueError, TypeError):
-                            raise errors.InvalidParameterValueError(err_msg)
-                    # list and something that can be transformed to a list
-                    elif type(default_parameters[k]) == types.ListType:
-                        try:
-                            parameters[k] = list(supplied_parameters[k])
-                        except TypeError:
-                            raise errors.InvalidParameterValueError(err_msg)
-                    else:
-                        raise errors.InvalidParameterValueError(err_msg)
-                else:
-                    raise errors.NonExistentParameterError(k, cls)
-        return parameters
-    
-    @classmethod
-    def translate(cls, parameters):
-        """Translate standardized model parameters to simulator-specific parameters."""
-        parameters = cls.checkParameters(parameters, with_defaults=False)
-        native_parameters = {}
-        for name in parameters:
-            D = cls.translations[name]
-            pname = D['translated_name']
-            if is_listlike(cls.default_parameters[name]):
-                parameters[name] = numpy.array(parameters[name])
-            try:
-                pval = eval(D['forward_transform'], globals(), parameters)
-            except NameError, errmsg:
-                raise NameError("Problem translating '%s' in %s. Transform: '%s'. Parameters: %s. %s" \
-                                % (pname, cls.__name__, D['forward_transform'], parameters, errmsg))
-            except ZeroDivisionError:
-                pval = 1e30 # this is about the highest value hoc can deal with
-            native_parameters[pname] = pval
-        return native_parameters
-    
-    @classmethod
-    def reverse_translate(cls, native_parameters):
-        """Translate simulator-specific model parameters to standardized parameters."""
-        standard_parameters = {}
-        for name,D  in cls.translations.items():
-            if is_listlike(cls.default_parameters[name]):
-                tname = D['translated_name']
-                native_parameters[tname] = numpy.array(native_parameters[tname])
-            try:
-                standard_parameters[name] = eval(D['reverse_transform'], {}, native_parameters)
-            except NameError, errmsg:
-                raise NameError("Problem translating '%s' in %s. Transform: '%s'. Parameters: %s. %s" \
-                                % (name, cls.__name__, D['reverse_transform'], native_parameters, errmsg))
-        return standard_parameters
-
-    @classmethod
-    def simple_parameters(cls):
-        """Return a list of parameters for which there is a one-to-one
-        correspondance between standard and native parameter values."""
-        return [name for name in cls.translations if cls.translations[name]['forward_transform'] == name]
-
-    @classmethod
-    def scaled_parameters(cls):
-        """Return a list of parameters for which there is a unit change between
-        standard and native parameter values."""
-        return [name for name in cls.translations if "float" in cls.translations[name]['forward_transform']]
-    
-    @classmethod
-    def computed_parameters(cls):
-        """Return a list of parameters whose values must be computed from
-        more than one other parameter."""
-        return [name for name in cls.translations if name not in cls.simple_parameters()+cls.scaled_parameters()]
-        
-    def update_parameters(self, parameters):
-        """
-        update self.parameters with those in parameters 
-        """
-        self.parameters.update(self.translate(parameters))
-        
-    def describe(self, template='standard'):
-        return str(self)
-    
-
-class StandardCellType(StandardModelType):
-    """Base class for standardized cell model classes."""
-
-    recordable = ['spikes', 'v', 'gsyn']
-    synapse_types = ('excitatory', 'inhibitory')
-    conductance_based = True # over-ride for cells with current-based synapses
-    always_local = False # over-ride for NEST spike sources
-
-
-class ModelNotAvailable(object):
-    """Not available for this simulator."""
-    
-    def __init__(self, *args, **kwargs):
-        raise NotImplementedError("The %s model is not available for this simulator." % self.__class__.__name__)
+
 
 # ==============================================================================
@@ -519 +337 @@
     # should refactor to eliminate this repetition
     logger.debug("connecting %s to %s on host %d" % (source, target, rank()))
-    if not is_listlike(source):
+    if not core.is_listlike(source):
         source = [source]
-    if not is_listlike(target):
+    if not core.is_listlike(target):
         target = [target]
     delay = check_delay(delay)
@@ -599 +417 @@
           e.g., (10,10) will create a two-dimensional population of size 10x10.
         cellclass should either be a standardized cell class (a class inheriting
-        from common.StandardCellType) or a string giving the name of the
+        from common.standardmodels.StandardCellType) or a string giving the name of the
         simulator-specific model that makes up the population.
         cellparams should be a dict which is passed to the neuron model
@@ -612 +430 @@
             assert isinstance(dims, tuple), "`dims` must be an integer or a tuple. You have supplied a %s" % type(dims)
         self.label = label or 'population%d' % Population.nPop         
-        if isinstance(cellclass, type) and issubclass(cellclass, StandardCellType):
+        if isinstance(cellclass, type) and issubclass(cellclass, standardmodels.StandardCellType):
             self.celltype = cellclass(cellparams)
         else:
@@ -853 +671 @@
     def can_record(self, variable):
         """Determine whether `variable` can be recorded from this population."""
-        if isinstance(self.celltype, StandardCellType):
+        if isinstance(self.celltype, standardmodels.StandardCellType):
             return (variable in self.celltype.recordable)
         else:
@@ -1110 +928 @@
                  connecting the neurons.
         
-        synapse_dynamics - a `SynapseDynamics` object specifying which
+        synapse_dynamics - a `standardmodels.SynapseDynamics` object specifying which
                  synaptic plasticity mechanisms to use.
         
@@ -1139 +957 @@
         self.long_term_plasticity_mechanism = None
         if self.synapse_dynamics:
-            assert isinstance(self.synapse_dynamics, SynapseDynamics), \
-              "The synapse_dynamics argument, if specified, must be a SynapseDynamics object, not a %s" % type(synapse_dynamics)
+            assert isinstance(self.synapse_dynamics, standardmodels.SynapseDynamics), \
+              "The synapse_dynamics argument, if specified, must be a standardmodels.SynapseDynamics object, not a %s" % type(synapse_dynamics)
             if self.synapse_dynamics.fast:
-                assert isinstance(self.synapse_dynamics.fast, ShortTermPlasticityMechanism)
+                assert isinstance(self.synapse_dynamics.fast, standardmodels.ShortTermPlasticityMechanism)
                 if hasattr(self.synapse_dynamics.fast, 'native_name'):
                     self.short_term_plasticity_mechanism = self.synapse_dynamics.fast.native_name
@@ -1149 +967 @@
                 self._short_term_plasticity_parameters = self.synapse_dynamics.fast.parameters.copy()
             if self.synapse_dynamics.slow:
-                assert isinstance(self.synapse_dynamics.slow, STDPMechanism)
+                assert isinstance(self.synapse_dynamics.slow, standardmodels.STDPMechanism)
                 assert 0 <= self.synapse_dynamics.slow.dendritic_delay_fraction <= 1.0
                 td = self.synapse_dynamics.slow.timing_dependence
@@ -1234 +1052 @@
         Set parameters of the synapse dynamics to values taken from rand_distr
         """
-        self.setSynapseDynamics(param, rand_distr.next(len(self)))
+        self.setstandardmodels.SynapseDynamics(param, rand_distr.next(len(self)))
     
     # --- Methods for writing/reading information to/from file. ----------------
@@ -1269 +1087 @@
         """
         if gather:
-            logger.error("getSynapseDynamics() with gather=True not yet implemented")
+            logger.error("getstandardmodels.SynapseDynamics() with gather=True not yet implemented")
         return self.connection_manager.get(parameter_name, format, offset=(self.pre.first_id, self.post.first_id))
     
@@ -1374 +1192 @@
             return Template(template).substitute(context)
 
-        
 # ==============================================================================
-#   Synapse Dynamics classes
-# ==============================================================================
-
-class SynapseDynamics(object):
-    """
-    For specifying synapse short-term (faciliation, depression) and long-term
-    (STDP) plasticity. To be passed as the `synapse_dynamics` argument to
-    `Projection.__init__()` or `connect()`.
-    """
-    
-    def __init__(self, fast=None, slow=None):
-        """
-        Create a new specification for a dynamic synapse, combining a `fast`
-        component (short-term facilitation/depression) and a `slow` component
-        (long-term potentiation/depression).
-        """
-        self.fast = fast
-        self.slow = slow
-    
-    def describe(self, template='standard'):
-        """
-        Return a human-readable description of the synaptic properties.
-        """
-        if template == 'standard':
-            lines = ["Short-term plasticity mechanism: $fast",
-                     "Long-term plasticity mechanism: $slow"]
-            template = "\n".join(lines)
-        context = {'fast': self.fast and self.fast.describe() or 'None',
-                   'slow': self.slow and self.slow.describe() or 'None'}
-        if template == None:
-            return context
-        else:
-            return Template(template).substitute(context)
-        
-        
-class ShortTermPlasticityMechanism(StandardModelType):
-    """Abstract base class for models of short-term synaptic dynamics."""
-    
-    def __init__(self):
-        raise NotImplementedError
-
-
-class STDPMechanism(object):
-    """Specification of STDP models."""
-    
-    def __init__(self, timing_dependence=None, weight_dependence=None,
-                 voltage_dependence=None, dendritic_delay_fraction=1.0):
-        """
-        Create a new specification for an STDP mechanism, by combining a
-        weight-dependence, a timing-dependence, and, optionally, a voltage-
-        dependence.
-        
-        For point neurons, the synaptic delay `d` can be interpreted either as
-        occurring purely in the pre-synaptic axon + synaptic cleft, in which
-        case the synaptic plasticity mechanism 'sees' the post-synaptic spike
-        immediately and the pre-synaptic spike after a delay `d`
-        (`dendritic_delay_fraction = 0`) or as occurring purely in the post-
-        synaptic dendrite, in which case the pre-synaptic spike is seen
-        immediately, and the post-synaptic spike after a delay `d`
-        (`dendritic_delay_fraction = 1`), or as having both pre- and post-
-        synaptic components (`dendritic_delay_fraction` between 0 and 1).
-        
-        In a future version of the API, we will allow the different
-        components of the synaptic delay to be specified separately in
-        milliseconds.
-        """
-        self.timing_dependence = timing_dependence
-        self.weight_dependence = weight_dependence
-        self.voltage_dependence = voltage_dependence
-        self.dendritic_delay_fraction = dendritic_delay_fraction
-        
-    def describe(self):
-        """
-        Return a human-readable description of the STDP mechanism.
-        """
-        return "STDP mechanism (this description needs to be filled out)."
-
-
-class STDPWeightDependence(StandardModelType):
-    """Abstract base class for models of STDP weight dependence."""
-    
-    def __init__(self):
-        raise NotImplementedError
-
-
-class STDPTimingDependence(StandardModelType):
-    """Abstract base class for models of STDP timing dependence (triplets, etc)"""
-    
-    def __init__(self):
-        raise NotImplementedError
-
-
-# ==============================================================================

trunk/src/connectors.py

@@ -20 +20 @@
                   fabs, floor, fmod, hypot, ldexp, log, log10, modf, pi, power, \
                   sin, sinh, sqrt, tan, tanh
-from pyNN import random, common, errors
+from pyNN import random, common, errors, core
 from pyNN.space import Space
 
@@ -116 +116 @@
             # returned.
             rarr = rng.next(N, 'uniform', (0, 1), mask_local=local)
-            if not common.is_listlike(rarr) and common.is_number(rarr): # if N=1, rarr will be a single number
+            if not core.is_listlike(rarr) and numpy.isscalar(rarr): # if N=1, rarr will be a single number
                 rarr = numpy.array([rarr])
-            if common.is_number(p):
+            if numpy.isscalar(p):
                 create = rarr < p
             else:

trunk/src/connectors2.py

@@ -1 +1 @@
 import numpy
-from pyNN import common
+from pyNN import common, core
 from pyNN.space import Space
 from pyNN.random import RandomDistribution
@@ -15 +15 @@
     def get(self, connectivity_matrix, distance_matrix):
         local_value_mask = self.local_mask[connectivity_matrix]
-        if common.is_number(self.source):
+        if numpy.isscalar(self.source):
             return numpy.ones((local_value_mask.sum(),))*self.source
         elif isinstance(self.source, RandomDistribution):
@@ -69 +69 @@
             self.delays = min_delay
         else:
-            if common.is_listlike(delays):
+            if core.is_listlike(delays):
                 assert min(delays) >= min_delay
             else:
@@ -169 +169 @@
             N = projection.post.size
             rarr = projection.rng.next(N, 'uniform', (0,1), mask_local=False)
-            if not common.is_listlike(rarr) and common.is_number(rarr): # if N=1, rarr will be a single number
+            if not core.is_listlike(rarr) and numpy.isscalar(rarr): # if N=1, rarr will be a single number
                 rarr = numpy.array([rarr])
             global_target_mask = rarr < p

trunk/src/nest/__init__.py

@@ -7 +7 @@
 import nest
 from pyNN.nest import simulator
-from pyNN import common, recording, errors, space, __doc__
+from pyNN import common, recording, errors, space, standardmodels, __doc__
 common.simulator = simulator
 recording.simulator = simulator
@@ -39 +39 @@
 def list_standard_models():
     """Return a list of all the StandardCellType classes available for this simulator."""
-    standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, common.StandardCellType)]
+    standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, standardmodels.StandardCellType)]
     for cell_class in standard_cell_types:
         try:
@@ -182 +182 @@
           e.g., (10,10) will create a two-dimensional population of size 10x10.
         cellclass should either be a standardized cell class (a class inheriting
-        from common.StandardCellType) or a string giving the name of the
+        from standardmodels.StandardCellType) or a string giving the name of the
         simulator-specific model that makes up the population.
         cellparams should be a dict which is passed to the neuron model

trunk/src/nest/cells.py

@@ -5 +5 @@
 """
 
-from pyNN import common, cells
+from pyNN import standardmodels, cells
  
 class IF_curr_alpha(cells.IF_curr_alpha):
@@ -13 +13 @@
     """
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'E_L'),
         ('v_reset',    'V_reset'),
@@ -35 +35 @@
     """
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'E_L'),
         ('v_reset',    'V_reset'),
@@ -56 +56 @@
     """
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'E_L')    ,
         ('v_reset',    'V_reset'),
@@ -79 +79 @@
     """
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'E_L')    ,
         ('v_reset',    'V_reset'),
@@ -107 +107 @@
     See also: EIF_cond_alpha_isfa_ista
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'E_L'),
         ('v_reset',    'V_reset'),
@@ -139 +139 @@
     # in 'iaf_cond_exp_sfa_rr', the dimension of C_m is pF, 
     # while in the pyNN context, cm is given in nF
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_reset',    'V_reset'),
         ('v_rest',     'E_L'),
@@ -162 +162 @@
     """Single-compartment Hodgkin-Huxley model."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('gbar_Na',    'g_Na'),   
         ('gbar_K',     'g_K'),    
@@ -192 +192 @@
     """
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_init'    , 'v_init'),
         ('w_init'    , 'w',         1000.0),  # nA -> pA
@@ -218 +218 @@
     """Spike source, generating spikes according to a Poisson process."""
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('rate',     'rate'),
         ('start',    'start'),
@@ -240 +240 @@
     """
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('a',        'a'),
         ('b',        'b'),
@@ -259 +259 @@
     """Spike source generating spikes at the times given in the spike_times array."""
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('spike_times', 'spike_times'),
     )

trunk/src/nest/simulator.py

@@ -27 +27 @@
 
 import nest
-from pyNN import common, random, errors
+from pyNN import common, random, errors, standardmodels, core
 import logging
 import numpy
@@ -150 +150 @@
         nest_model = cellclass
         cell_parameters = cellparams or {}
-    elif isinstance(cellclass, type) and issubclass(cellclass, common.StandardCellType):
+    elif isinstance(cellclass, type) and issubclass(cellclass, standardmodels.StandardCellType):
         celltype = cellclass(cellparams)
         nest_model = celltype.nest_name
@@ -310 +310 @@
         """
         # are we sure the targets are all on the current node?
-        if common.is_listlike(source):
+        if core.is_listlike(source):
             assert len(source) == 1
             source = source[0]
-        if not common.is_listlike(targets):
+        if not core.is_listlike(targets):
             targets = [targets]
         assert len(targets) > 0
@@ -351 +351 @@
         """
         # are we sure the targets are all on the current node?
-        if common.is_listlike(target):
+        if core.is_listlike(target):
             assert len(target) == 1
             target = target[0]
-        if not common.is_listlike(sources):
+        if not core.is_listlike(sources):
             sources = [sources]
         assert len(sources) > 0, sources
@@ -452 +452 @@
                    matrix (as returned by `get(format='array')`).
         """
-        if not (common.is_number(value) or common.is_listlike(value)):
+        if not (numpy.isscalar(value) or core.is_listlike(value)):
             raise TypeError("Argument should be a numeric type (int, float...), a list, or a numpy array.")   
         
@@ -470 +470 @@
                     value_list.append(val)
             value = value_list
-        if common.is_listlike(value):
+        if core.is_listlike(value):
             value = numpy.array(value)
         else:

trunk/src/nest/synapses.py

@@ -5 +5 @@
 """
 
-from pyNN import common, synapses
+from pyNN import standardmodels, synapses
 
 
-SynapseDynamics = common.SynapseDynamics
+SynapseDynamics = standardmodels.SynapseDynamics
 
-class STDPMechanism(common.STDPMechanism):
+class STDPMechanism(standardmodels.STDPMechanism):
     """Specification of STDP models."""
     
@@ -18 +18 @@
                                                  for the purpose of STDP calculations all delays
                                                  are assumed to be dendritic."""
-        common.STDPMechanism.__init__(self, timing_dependence, weight_dependence,
+        standardmodels.STDPMechanism.__init__(self, timing_dependence, weight_dependence,
                                       voltage_dependence, dendritic_delay_fraction)
 
@@ -24 +24 @@
 class TsodyksMarkramMechanism(synapses.TsodyksMarkramMechanism):
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('U', 'U'),
         ('tau_rec', 'tau_rec'),
@@ -48 +48 @@
     """
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wmax',  1000.0), # unit conversion
         ('w_min',     'w_min_always_zero_in_NEST'),
@@ -73 +73 @@
     For potentiation, Dw propto w_max-w
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wmax',  1000.0), # unit conversion
         ('w_min',     'w_min_always_zero_in_NEST'),
@@ -96 +96 @@
     depression (Dw propto w-w_min) and is fixed for potentiation.
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wmax',  1000.0), # unit conversion
         ('w_min',     'w_min_always_zero_in_NEST'),
@@ -121 +121 @@
     For potentiation, Dw propto w_max-w
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wmax',  1000.0), # unit conversion
         ('w_min',     'w_min_always_zero_in_NEST'),
@@ -142 +142 @@
 class SpikePairRule(synapses.SpikePairRule):
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_plus',  'tau_plus'),
         ('tau_minus', 'tau_minus'), # defined in post-synaptic neuron

trunk/src/neuron/__init__.py

@@ -9 +9 @@
 from pyNN.random import *
 from pyNN.neuron import simulator
-from pyNN import common, recording, space, __doc__
+from pyNN import common, recording, space, standardmodels, __doc__
 common.simulator = simulator
 recording.simulator = simulator
@@ -30 +30 @@
 def list_standard_models():
     """Return a list of all the StandardCellType classes available for this simulator."""
-    return [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, common.StandardCellType)]
+    return [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, standardmodels.StandardCellType)]
 
 # ==============================================================================
@@ -121 +121 @@
           e.g., (10,10) will create a two-dimensional population of size 10x10.
         cellclass should either be a standardized cell class (a class inheriting
-        from common.StandardCellType) or a string giving the name of the
+        from standardmodels.StandardCellType) or a string giving the name of the
         simulator-specific model that makes up the population.
         cellparams should be a dict which is passed to the neuron model

trunk/src/neuron/cells.py

@@ -6 +6 @@
 """
 
-from pyNN import common, cells, errors
+from pyNN import standardmodels, cells, errors
 from neuron import h, nrn, hclass
 from math import pi
@@ -425 +425 @@
     shaped post-synaptic current."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'tau_m'),
         ('cm',         'c_m'),
@@ -450 +450 @@
     excitatory and inhibitory synapses."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'tau_m'),
         ('cm',         'c_m'),
@@ -474 +474 @@
     shaped post-synaptic conductance."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'tau_m'),
         ('cm',         'c_m'),
@@ -501 +501 @@
     exponentially-decaying post-synaptic conductance."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'tau_m'),
         ('cm',         'c_m'),
@@ -531 +531 @@
     """
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_rest',     'v_rest'),
         ('v_thresh',   'v_thresh'),
@@ -554 +554 @@
 class HH_cond_exp(cells.HH_cond_exp):
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('gbar_Na',    'gbar_Na', 1e-6),   
         ('gbar_K',     'gbar_K', 1e-6),    
@@ -581 +581 @@
     """Spike source, generating spikes according to a Poisson process."""
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('start',    'start'),
         ('rate',     '_interval',  "1000.0/rate",  "1000.0/_interval"),
@@ -592 +592 @@
     """Spike source generating spikes at the times given in the spike_times array."""
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('spike_times', 'spike_times'),
     )
@@ -609 +609 @@
     """
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('v_init',     'v_init'),
         ('w_init',     'w_init'),

trunk/src/neuron/simulator.py

@@ -29 +29 @@
 
 from pyNN import __path__ as pyNN_path
-from pyNN import common, random, errors
+from pyNN import common, random, errors, standardmodels, core
 import platform
 import logging
@@ -284 +284 @@
             raise errors.InvalidModelError("There is no hoc template called %s" % cellclass)
         cell_parameters = cellparams or {}
-    elif isinstance(cellclass, type) and issubclass(cellclass, common.StandardCellType):
+    elif isinstance(cellclass, type) and issubclass(cellclass, standardmodels.StandardCellType):
         celltype = cellclass(cellparams)
         cell_model = celltype.model
@@ -464 +464 @@
             errmsg = "Invalid source ID: %s (gid_counter=%d)" % (source, state.gid_counter)
             raise errors.ConnectionError(errmsg)
-        if not common.is_listlike(targets):
+        if not core.is_listlike(targets):
             targets = [targets]
         if isinstance(weights, float):
@@ -533 +533 @@
                    matrix (as returned by `get(format='array')`).
         """
-        if common.is_number(value):
+        if numpy.isscalar(value):
             for c in self:
                 setattr(c, name, value)
@@ -548 +548 @@
                 else:
                     setattr(c, name, val)
-        elif common.is_listlike(value):
+        elif core.is_listlike(value):
             for c,val in zip(self.connections, value):
                 setattr(c, name, val)

trunk/src/neuron/synapses.py

@@ -5 +5 @@
 """
 
-from pyNN import common, synapses
+from pyNN import standardmodels, synapses
 
-SynapseDynamics = common.SynapseDynamics
-STDPMechanism = common.STDPMechanism
+SynapseDynamics = standardmodels.SynapseDynamics
+STDPMechanism = standardmodels.STDPMechanism
 
 class TsodyksMarkramMechanism(synapses.TsodyksMarkramMechanism):
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('U', 'U'),
         ('tau_rec', 'tau_rec'),
@@ -37 +37 @@
     """
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'wmax'),
         ('w_min',     'wmin'),
@@ -57 +57 @@
     For potentiation, Dw propto w_max-w
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'wmax'),
         ('w_min',     'wmin'),
@@ -75 +75 @@
     depression (Dw propto w-w_min) and is fixed for potentiation
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'wmax'),
         ('w_min',     'wmin'),
@@ -94 +94 @@
 class SpikePairRule(synapses.SpikePairRule):
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_plus',  'tauLTP'),
         ('tau_minus', 'tauLTD'),

trunk/src/pcsim/__init__.py

@@ -15 +15 @@
 
 import pyNN.random
-from pyNN import common, recording, errors, space, __doc__
+from pyNN import common, recording, errors, space, core, __doc__
 from pyNN.pcsim import simulator
 common.simulator = simulator
@@ -74 +74 @@
     """Return a list of all the StandardCellType classes available for this simulator."""
     setup()
-    standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, common.StandardCellType)]
+    standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, standardmodels.StandardCellType)]
     for cell_class in standard_cell_types:
         try:
@@ -261 +261 @@
 #        if type(source) != types.ListType and type(target) != types.ListType:
 #            connections = simulator.net.connect(source, target, syn_factory)
-#            if not common.is_listlike(connections):
+#            if not core.is_listlike(connections):
 #                connections = [connections]
 #            return connections
@@ -304 +304 @@
           e.g., (10,10) will create a two-dimensional population of size 10x10.
         cellclass should either be a standardized cell class (a class inheriting
-        from common.StandardCellType) or a string giving the name of the
+        from standardmodels.StandardCellType) or a string giving the name of the
         simulator-specific model that makes up the population.
         cellparams should be a dict which is passed to the neuron model
@@ -336 +336 @@
             cellclass = getattr(pypcsim, cellclass)
             self.celltype = cellclass
-        if issubclass(cellclass, common.StandardCellType):
+        if issubclass(cellclass, standardmodels.StandardCellType):
             self.celltype = cellclass(cellparams)
             self.cellfactory = self.celltype.simObjFactory
@@ -582 +582 @@
         ##
         # handle synapse dynamics
-        if common.is_listlike(method.weights):
+        if core.is_listlike(method.weights):
             w = method.weights[0]
         elif hasattr(method.weights, "next"): # random distribution
@@ -588 +588 @@
         else:
             w = method.weights
-        if common.is_listlike(method.delays):
+        if core.is_listlike(method.delays):
             d = min(method.delays)
         elif hasattr(method.delays, "next"): # random distribution

trunk/src/pcsim/cells.py

@@ -5 +5 @@
 """
 
-from pyNN import common, cells, errors
+from pyNN import common, cells, errors, standardmodels
 import pypcsim
 import numpy
@@ -16 +16 @@
     shaped post-synaptic current."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'taum',      1e-3),
         ('cm',         'Cm',        1e-9), 
@@ -43 +43 @@
        excitatory and inhibitory synapses."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'taum',      1e-3),
         ('cm',         'Cm',        1e-9), 
@@ -69 +69 @@
     shaped post-synaptic conductance."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'taum',      1e-3),
         ('cm',         'Cm',        1e-9), 
@@ -98 +98 @@
     exponentially-decaying post-synaptic conductance."""
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_m',      'taum',      1e-3),
         ('cm',         'Cm',        1e-9), 
@@ -127 +127 @@
     """Spike source, generating spikes according to a Poisson process."""
 
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('start',    'Tstart',   1e-3), 
         ('rate',     'rate'), 
@@ -165 +165 @@
 class SpikeSourceArray(cells.SpikeSourceArray):
     """Spike source generating spikes at the times given in the spike_times array."""
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('spike_times', 'spikeTimes'), # 1e-3), 
     )
@@ -201 +201 @@
         return standard_parameters
 
-class EIF_cond_alpha_isfa_ista(common.ModelNotAvailable):
+class EIF_cond_alpha_isfa_ista(standardmodels.ModelNotAvailable):
     pass
 #class EIF_cond_alpha_isfa_ista(cells.EIF_cond_alpha_isfa_ista):
@@ -214 +214 @@
 #    """
 #
-#    translations = common.build_translations(
+#    translations = standardmodels.build_translations(
 #        ('v_init'    , 'Vinit',     1e-3),  # mV -> V
 #        ('w_init'    , 'w',         1e-9),  # nA -> A
@@ -248 +248 @@
 #        self.simObjFactory = getattr(pypcsim, EIF_cond_alpha_isfa_ista.pcsim_name)(**limited_parameters)
 
-class IF_facets_hardware1(common.ModelNotAvailable):
-    pass
-
-class HH_cond_exp(common.ModelNotAvailable):
+class IF_facets_hardware1(standardmodels.ModelNotAvailable):
+    pass
+
+class HH_cond_exp(standardmodels.ModelNotAvailable):
     pass
 
@@ -257 +257 @@
 #    """docstring needed here."""
 #    
-#    translations = common.build_translations(
+#    translations = standardmodels.build_translations(
 #        ('gbar_Na',    'gbar_Na'),   
 #        ('gbar_K',     'gbar_K'),    
@@ -284 +284 @@
         
         
-class SpikeSourceInhGamma(common.ModelNotAvailable):
-    pass
-
-class IF_cond_exp_gsfa_grr(common.ModelNotAvailable):
-    pass
+class SpikeSourceInhGamma(standardmodels.ModelNotAvailable):
+    pass
+
+class IF_cond_exp_gsfa_grr(standardmodels.ModelNotAvailable):
+    pass

trunk/src/pcsim/simulator.py

@@ -30 +30 @@
 import types
 import numpy
-from pyNN import common, errors
+from pyNN import common, errors, standardmodels, core
 
 recorder_list = []
@@ -150 +150 @@
     assert n > 0, 'n must be a positive integer'
     if isinstance(cellclass, type):
-        if issubclass(cellclass, common.StandardCellType):
+        if issubclass(cellclass, standardmodels.StandardCellType):
             cellfactory = cellclass(cellparams).simObjFactory
         elif issubclass(cellclass, pypcsim.SimObject):
@@ -276 +276 @@
             errmsg = "Invalid source ID: %s" % source
             raise errors.ConnectionError(errmsg)
-        if not common.is_listlike(targets):
+        if not core.is_listlike(targets):
             targets = [targets]
         if isinstance(weights, float):
@@ -352 +352 @@
                    matrix (as returned by `get(format='array')`)
         """
-        if common.is_number(value):
+        if numpy.isscalar(value):
             for c in self:
                 setattr(c, name, value)
@@ -367 +367 @@
                 else:
                     setattr(c, name, val)
-        elif common.is_listlike(value):
+        elif core.is_listlike(value):
             for c,val in zip(self.connections, value):
                 setattr(c, name, val)

trunk/src/pcsim/synapses.py

@@ -5 +5 @@
 """
 
-from pyNN import common, synapses
+from pyNN import standardmodels, synapses
 import pypcsim
 
@@ -33 +33 @@
                              ])
 
-SynapseDynamics = common.SynapseDynamics
+SynapseDynamics = standardmodels.SynapseDynamics
         
-class STDPMechanism(common.STDPMechanism):
+class STDPMechanism(standardmodels.STDPMechanism):
     """Specification of STDP models."""
     
@@ -41 +41 @@
                  voltage_dependence=None, dendritic_delay_fraction=1.0):
         # not sure what the situation is with dendritic_delay_fraction in PCSIM
-        common.STDPMechanism.__init__(self, timing_dependence, weight_dependence,
+        standardmodels.STDPMechanism.__init__(self, timing_dependence, weight_dependence,
                                       voltage_dependence, dendritic_delay_fraction)
 
@@ -47 +47 @@
 class TsodyksMarkramMechanism(synapses.TsodyksMarkramMechanism):
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('U', 'U'),
         ('tau_rec', 'D', 1e-3),
@@ -74 +74 @@
     """
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wex',  1e-9), # unit conversion. This exposes a limitation of the current
                                      # translation machinery, because this value depends on the
@@ -106 +106 @@
     For potentiation, Dw propto w_max-w
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wex',  1e-9), # unit conversion
         ('w_min',     'w_min_always_zero_in_PCSIM'),
@@ -133 +133 @@
     depression (Dw propto w-w_min) and is fixed for potentiation.
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wex',  1e-9), # unit conversion
         ('w_min',     'w_min_always_zero_in_PCSIM'),
@@ -160 +160 @@
     For potentiation, Dw propto w_max-w
     """
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('w_max',     'Wex',  1e-9), # unit conversion
         ('w_min',     'w_min_always_zero_in_PCSIM'),
@@ -183 +183 @@
 class SpikePairRule(synapses.SpikePairRule):
     
-    translations = common.build_translations(
+    translations = standardmodels.build_translations(
         ('tau_plus',  'taupos', 1e-3),
         ('tau_minus', 'tauneg', 1e-3), 

trunk/src/synapses.py

@@ -16 +16 @@
 """
 
-from common import ShortTermPlasticityMechanism, STDPWeightDependence, STDPTimingDependence
+from standardmodels import ShortTermPlasticityMechanism, STDPWeightDependence, STDPTimingDependence
 
 class TsodyksMarkramMechanism(ShortTermPlasticityMechanism):