def __init__(self, pre, post, target, synapse=None, name=None):
"""
*Parameters*:
* **pre**: pre-synaptic population (either its name or a ``Population`` object).
* **post**: post-synaptic population (either its name or a ``Population`` object).
* **target**: type of the connection.
* **synapse**: a ``Synapse`` instance.
* **name**: unique name of the projection (optional).
By default, the synapse only ensures linear synaptic transmission:
* For rate-coded populations: ``psp = w * pre.r``
* For spiking populations: ``g_target += w``
"""
# Store the pre and post synaptic populations
# the user provide either a string or a population object
# in case of string, we need to search for the corresponding object
if isinstance(pre, str):
for pop in Global._network[0]['populations']:
if pop.name == pre:
self.pre = pop
else:
self.pre = pre
if isinstance(post, str):
for pop in Global._network[0]['populations']:
if pop.name == post:
self.post = pop
else:
self.post = post
# Store the arguments
self.target = target
# Add the target to the postsynaptic population
self.post.targets.append(self.target)
# check if a synapse description is attached
if not synapse:
# No synapse attached assume default synapse based on
# presynaptic population.
if self.pre.neuron_type.type == 'rate':
from ANNarchy.models.Synapses import DefaultRateCodedSynapse
self.synapse_type = DefaultRateCodedSynapse()
self.synapse_type.type = 'rate'
else:
from ANNarchy.models.Synapses import DefaultSpikingSynapse
self.synapse_type = DefaultSpikingSynapse()
self.synapse_type.type = 'spike'
elif inspect.isclass(synapse):
self.synapse_type = synapse()
self.synapse_type.type = self.pre.neuron_type.type
else:
self.synapse_type = copy.deepcopy(synapse)
self.synapse_type.type = self.pre.neuron_type.type
# Analyse the parameters and variables
self.synapse_type._analyse()
# Create a default name
self.id = len(Global._network[0]['projections'])
if name:
self.name = name
else:
self.name = 'proj'+str(self.id)
# Get a list of parameters and variables
self.parameters = []
self.init = {}
for param in self.synapse_type.description['parameters']:
self.parameters.append(param['name'])
self.init[param['name']] = param['init']
self.variables = []
for var in self.synapse_type.description['variables']:
self.variables.append(var['name'])
self.init[var['name']] = var['init']
self.attributes = self.parameters + self.variables
# Add the population to the global network
Global._network[0]['projections'].append(self)
# Finalize initialization
self.initialized = False
# Cython instance
self.cyInstance = None
# Connectivity
self._synapses = None
self._connection_method = None
self._connection_args = None
self._connection_delay = None
self._connector = None
# If a single weight value is used
self._single_constant_weight = False
# If a dense matrix should be used instead of LIL
self._dense_matrix = False
# Recorded variables
self.recorded_variables = {}
# Reporting
self.connector_name = "Specific"
self.connector_description = "Specific"
# Overwritten by derived classes, to add
# additional code
self._specific_template = {}
# To allow case-specific adjustment of parallelization
# parameters, e. g. openMP schedule, we introduce a
# dictionary read by the ProjectionGenerator.
#
# Will be overwritten either by inherited classes or
# by an omp_config provided to the compile() method.
self._omp_config = {
#'psp_schedule': 'schedule(dynamic)'
}
def __init__(self, pre, post, target, synapse=None, name=None, copied=False):
"""
*Parameters*:
* **pre**: pre-synaptic population (either its name or a ``Population`` object).
* **post**: post-synaptic population (either its name or a ``Population`` object).
* **target**: type of the connection.
* **synapse**: a ``Synapse`` instance.
* **name**: unique name of the projection (optional, it defaults to ``proj0``, ``proj1``, etc).
By default, the synapse only ensures linear synaptic transmission:
* For rate-coded populations: ``psp = w * pre.r``
* For spiking populations: ``g_target += w``
"""
# Check if the network has already been compiled
if Global._network[0]['compiled'] and not copied:
Global._error('you cannot add a projection after the network has been compiled.')
# Store the pre and post synaptic populations
# the user provide either a string or a population object
# in case of string, we need to search for the corresponding object
if isinstance(pre, str):
for pop in Global._network[0]['populations']:
if pop.name == pre:
self.pre = pop
else:
self.pre = pre
if isinstance(post, str):
for pop in Global._network[0]['populations']:
if pop.name == post:
self.post = pop
else:
self.post = post
# Store the arguments
if isinstance(target, list) and len(target) == 1:
self.target = target[0]
else:
self.target = target
# Add the target to the postsynaptic population
self.post.targets.append(self.target)
# check if a synapse description is attached
if not synapse:
# No synapse attached assume default synapse based on
# presynaptic population.
if self.pre.neuron_type.type == 'rate':
from ANNarchy.models.Synapses import DefaultRateCodedSynapse
self.synapse_type = DefaultRateCodedSynapse()
self.synapse_type.type = 'rate'
else:
from ANNarchy.models.Synapses import DefaultSpikingSynapse
self.synapse_type = DefaultSpikingSynapse()
self.synapse_type.type = 'spike'
elif inspect.isclass(synapse):
self.synapse_type = synapse()
self.synapse_type.type = self.pre.neuron_type.type
else:
self.synapse_type = copy.deepcopy(synapse)
self.synapse_type.type = self.pre.neuron_type.type
# Analyse the parameters and variables
self.synapse_type._analyse()
# Create a default name
self.id = len(Global._network[0]['projections'])
if name:
self.name = name
else:
self.name = 'proj'+str(self.id)
# Get a list of parameters and variables
self.parameters = []
self.init = {}
for param in self.synapse_type.description['parameters']:
self.parameters.append(param['name'])
self.init[param['name']] = param['init']
self.variables = []
for var in self.synapse_type.description['variables']:
self.variables.append(var['name'])
self.init[var['name']] = var['init']
self.attributes = self.parameters + self.variables
# Get a list of user-defined functions
self.functions = [func['name'] for func in self.synapse_type.description['functions']]
# Add the population to the global network
Global._network[0]['projections'].append(self)
# Finalize initialization
self.initialized = False
# Cython instance
self.cyInstance = None
# Connectivity
self._synapses = None
self._connection_method = None
self._connection_args = None
self._connection_delay = None
self._connector = None
# List of post ranks is full by default, will be changed when the weights are created
self.post_ranks = list(range(self.post.size))
# Default configuration for connectivity
self._storage_format = "lil"
self._storage_order = "post_to_pre"
# If a single weight value is used
self._single_constant_weight = False
# If a dense matrix should be used instead of LIL
self._dense_matrix = False
# Reporting
self.connector_name = "Specific"
self.connector_description = "Specific"
# Overwritten by derived classes, to add
# additional code
self._specific_template = {}
# Set to false by derived classes to prevent saving of
# data, e. g. in case of weight-sharing projections
self._saveable = True
# To allow case-specific adjustment of parallelization
# parameters, e. g. openMP schedule, we introduce a
# dictionary read by the ProjectionGenerator.
#
# Will be overwritten either by inherited classes or
# by an omp_config provided to the compile() method.
self._omp_config = {
#'psp_schedule': 'schedule(dynamic)'
}
