def uniform_network(cls, size, neuron_model=default_neuron,
neuron_param=None, syn_model=default_synapse,
syn_param=None):
'''
Generate a network containing only one type of neurons.
Parameters
----------
size : int
Number of neurons in the network.
neuron_model : string, optional (default: 'aief_cond_alpha')
Name of the NEST neural model to use when simulating the activity.
neuron_param : dict, optional (default: {})
Dictionary containing the neural parameters; the default value will
make NEST use the default parameters of the model.
syn_model : string, optional (default: 'static_synapse')
NEST synaptic model to use when simulating the activity.
syn_param : dict, optional (default: {})
Dictionary containing the synaptic parameters; the default value
will make NEST use the default parameters of the model.
Returns
-------
net : :class:`~nngt.Network` or subclass
Uniform network of disconnected neurons.
'''
if neuron_param is None:
neuron_param = {}
if syn_param is None:
syn_param = {}
pop = NeuralPop.uniform_population(size, None, neuron_model,
neuron_param, syn_model, syn_param)
net = cls(population=pop)
return net
def ei_network(cls, size, ei_ratio=0.2, en_model=default_neuron,
en_param=None, es_model=default_synapse, es_param=None,
in_model=default_neuron, in_param=None, is_model=default_synapse,
is_param=None):
'''
Generate a network containing a population of two neural groups:
inhibitory and excitatory neurons.
Parameters
----------
size : int
Number of neurons in the network.
ei_ratio : double, optional (default: 0.2)
Ratio of inhibitory neurons: :math:`\\frac{N_i}{N_e+N_i}`.
en_model : string, optional (default: 'aeif_cond_alpha')
Nest model for the excitatory neuron.
en_param : dict, optional (default: {})
Dictionary of parameters for the the excitatory neuron.
es_model : string, optional (default: 'static_synapse')
NEST model for the excitatory synapse.
es_param : dict, optional (default: {})
Dictionary containing the excitatory synaptic parameters.
in_model : string, optional (default: 'aeif_cond_alpha')
Nest model for the inhibitory neuron.
in_param : dict, optional (default: {})
Dictionary of parameters for the the inhibitory neuron.
is_model : string, optional (default: 'static_synapse')
NEST model for the inhibitory synapse.
is_param : dict, optional (default: {})
Dictionary containing the inhibitory synaptic parameters.
Returns
-------
net : :class:`~nngt.Network` or subclass
Network of disconnected excitatory and inhibitory neurons.
'''
if en_param is None:
en_param = {}
if es_param is None:
es_param = {}
if in_param is None:
in_param = {}
if is_param is None:
is_param = {}
pop = NeuralPop.exc_and_inhib(size, ei_ratio, None, en_model, en_param,
es_model, es_param, in_model, in_param, is_model, is_param)
net = cls(population=pop)
return net