def __init__(self, config, exc_weights, inh_weights):
b.Network.__init__(self)
Configurable.__init__(self, config)
self._add_config_value('stimulus_duration', quantity)
builder = TrainedLearningModelBuilder(config['model'])
self.input_gen = inputs.GroupedSpikeTimesGenerator(
config['inputs'], self.stimulus_duration)
self.indexing_scheme = self.input_gen.get_indexing_scheme()
self.neuron = builder.build_neuron_group()
self.input_neurons = b.SpikeGeneratorGroup(
self.input_gen.num_trains, inputs.swap_tuple_values(self.input_gen))
self.input_groups = ModelInputGroups(
self.indexing_scheme, self.input_neurons)
self.exc_synapses = builder.build_exc_synapses(
self.input_groups.excitatory, self.neuron, exc_weights)
self.inh_synapses = builder.build_inh_synapses(
self.input_groups.inhibitory, self.neuron, inh_weights)
self.add(
self.neuron, self.input_neurons, self.inh_synapses,
self.exc_synapses)
def __init__(self, config, indata=None):
b.Network.__init__(self)
builder = ModelBuilder(config['model'])
if indata is None:
self.input_gen = inputs.GroupedSpikeTimesGenerator(config['inputs'])
self.input_neurons = b.SpikeGeneratorGroup(
self.input_gen.num_trains,
inputs.swap_tuple_values(self.input_gen))
else:
self.input_gen = inputs.StoredSpikeTimesProvider(indata)
self.input_neurons = b.SpikeGeneratorGroup(
self.input_gen.num_trains, self.input_gen)
self.indexing_scheme = self.input_gen.get_indexing_scheme()
self.neuron = builder.build_neuron_group()
self.input_groups = ModelInputGroups(
self.indexing_scheme, self.input_neurons)
self.exc_synapses = builder.build_exc_synapses(
self.input_groups.excitatory, self.neuron,
self.tuning_function(self.input_groups.exc_group_membership, 5))
self.total_exc_weight_l1 = np.sum(self.exc_synapses.w[:, :])
self.total_exc_weight_l2 = linalg.norm(self.exc_synapses.w[:, :])
self.inh_synapses = builder.build_inh_synapses(
self.input_groups.inhibitory, self.neuron)
@b.network_operation
def normalize_exc_synapses_mult_l1():
self.exc_synapses.w[:, :] = \
self.total_exc_weight_l1 * self.exc_synapses.w[:, :] / np.sum(
self.exc_synapses.w[:, :])
@b.network_operation
def normalize_exc_synapses_mult_l2():
self.exc_synapses.w[:, :] = \
self.total_exc_weight_l2 * self.exc_synapses.w[:, :] / linalg.norm(
self.exc_synapses.w[:, :].flat)
@b.network_operation
def normalize_exc_synapses_add_l1():
self.exc_synapses.w[:, :] -= \
(np.sum(self.exc_synapses.w[:, :]) - self.total_exc_weight_l1) / \
self.exc_synapses.w[:, :].shape[0]
self.exc_synapses.w[:, :] = np.maximum(0, self.exc_synapses.w[:, :])
self.inh_synapses.w[:, :] = np.maximum(0, self.inh_synapses.w[:, :])
@b.network_operation
def normalize_exc_synapses_add_l2():
self.exc_synapses.w[:, :] -= \
(linalg.norm(self.exc_synapses.w[:, :]) -
self.total_exc_weight_l2) / self.exc_synapses.w[:, :].shape[0]
self.exc_synapses.w[:, :] = np.maximum(0, self.exc_synapses.w[:, :])
self.inh_synapses.w[:, :] = np.maximum(0, self.inh_synapses.w[:, :])
@b.network_operation
def noop():
pass
normalizations = {
'mult_l1': normalize_exc_synapses_mult_l1,
'add_l1': normalize_exc_synapses_add_l1,
'mult_l2': normalize_exc_synapses_mult_l2,
'add_l2': normalize_exc_synapses_add_l2,
'none': noop
}
self.add(
self.neuron, self.input_neurons, self.inh_synapses,
self.exc_synapses, normalizations[config['model']['normalization']])
