docs: def plot_correlation(z_vec, x0, x1, ax, lag=20., title='firing_rate vs LFP'):
      ''' mls
      on axes plot the correlation between x0 and x1
      
      args:
      ::
          x0 : first dataset
          x1 : second dataset - e.g., the multichannel LFP
          ax : matplotlib.axes.AxesSubplot object
          title : text to be used as current axis object title
      '''
      zvec = np.r_[z_vec]
      zvec = np.r_[zvec, zvec[-1] + np.diff(zvec)[-1]]
      
      xcorr_all=np.zeros((np.size(z_vec), x0.shape[0]))
      for i, z in enumerate(z_vec):
          x2 = x1[i, ]
          xcorr1 = np.correlate(normalize(x0),
                                normalize(x2), 'same') / x0.size
          xcorr_all[i,:]=xcorr1  
  
      # Find limits for the plot
      vlim = abs(xcorr_all).max()
      vlimround = 2.**np.round(np.log2(vlim))
      
      yticklabels=[]
      yticks = []
      ylimfound=np.zeros((1,2))
      for i, z in enumerate(z_vec):
          ind = np.arange(x0.size) - x0.size/2
          ax.plot(ind, xcorr_all[i,::-1] * 100. / vlimround + z, 'k',
                  clip_on=True, rasterized=False)
          yticklabels.append('ch. %i' %(i+1))
          yticks.append(z)    
  
      remove_axis_junk(ax)
      ax.set_title(title)
      ax.set_xlabel(r'lag $\tau$ (ms)')
  
      ax.set_xlim(-lag, lag)
      ax.set_ylim(z-100, 100)
      
      axis = ax.axis()
      ax.vlines(0, axis[2], axis[3], 'r', 'dotted')
  
      ax.yaxis.set_ticks(yticks)
      ax.yaxis.set_ticklabels(yticklabels)
  
      ax.xaxis.set_ticks_position('bottom')
      ax.yaxis.set_ticks_position('left')
  
      # Create a scaling bar
      ax.plot([lag, lag],
          [0, 100], lw=2, color='k', clip_on=False)
      ax.text(lag, 50, r'CC=%.2f' % vlimround,
              rotation='vertical', va='center')