code: def _synDelayParams(self):
          '''
          set up the detailed synaptic delay parameters,
          loc is mean delay,
          scale is std with low bound cutoff,
          assumes numpy.random.normal is used later
          '''
          delays = {}
          #mean delays
          loc = np.zeros((len(self.y), len(self.X)))
          loc[:, 0] = self.delays[0]
          loc[:, 1::2] = self.delays[0]
          loc[:, 2::2] = self.delays[1]
          #standard deviations
          scale = loc * self.delay_rel_sd
          
          #prepare output
          delay_loc = {}
          for i, y in enumerate(self.y):
              delay_loc.update({y : loc[i]})
          
          delay_scale = {}
          for i, y in enumerate(self.y):
              delay_scale.update({y : scale[i]})
                  
          return delay_loc, delay_scalf _synDelayParams(self):
          '''
          set up the detailed synaptic delay parameters,
          loc is mean delay,
          scale is std with low bound cutoff,
          assumes numpy.random.normal is used later
          '''
          delays = {}
          #mean delays
          loc = np.zeros((len(self.y), len(self.X)))
          loc[:, 0] = self.delays[0]
          loc[:, 1::2] = self.delays[0]
          loc[:, 2::2] = self.delays[1]
          #standard deviations
          scale = loc * self.delay_rel_sd
          
          #prepare output
          delay_loc = {}
          for i, y in enumerate(self.y):
              delay_loc.update({y : loc[i]})
          
          delay_scale = {}
          for i, y in enumerate(self.y):
              delay_scale.update({y : scale[i]})
                  
          return delay_loc, delay_scale
docs: set up the detailed synaptic delay parameters,
          loc is mean delay,
          scale is std with low bound cutoff,
          assumes numpy.random.normal is used later