Normalization

This module can be used to normalize all synapses connected to a NeuronGroup of a given type. The Normalization module follows a simple logic: At each time step, we sum the synaptic weights of all synapses selected by a tag, here GLUTAMATE, in the temporary variable temp_weight_sum. Then, we use this variable to normalize the weights. If the normalized sum of weights should be other than one, we can use the scaling variable norm_factor.

Because the normalization can include multiple synapse groups, summation and division is performed during for-loops. To avoid division by zero, each calculated normalization value is increased by one if it is zero before the scaled normalization is applied.

# /Examples_Paper/STDP_Hom_Norm/Normalization.py
from PymoNNto.NetworkCore.Behavior import *

class Normalization(Behavior):

    def initialize(self, neurons):
        self.syn_type = self.parameter('syn_type', 'GLUTAMATE', neurons)
        self.norm_factor = self.parameter('norm_factor', 1.0, neurons)
        neurons.temp_weight_sum = neurons.vector()

    def iteration(self, neurons):

        neurons.temp_weight_sum *= 0.0

        for s in neurons.synapses(afferent, self.syn_type):
            s.dst.temp_weight_sum += np.sum(np.abs(s.W), axis=1)

        neurons.temp_weight_sum /= self.norm_factor

        for s in neurons.synapses(afferent, self.syn_type):
            s.W = s.W / (s.dst.temp_weight_sum[:, None] + (s.dst.temp_weight_sum[:, None] == 0))