code: def template_dynamic_hhn(
      num_osc,
      steps,
      time,
      stimulus=None,
      params=None,
      separate=False,
      ccore_flag=False,
  ):
      net = hhn_network(num_osc, stimulus, params, ccore=ccore_flag)
  
      (t, dyn_peripheral, dyn_central) = net.simulate(steps, time)
  
      amount_canvases = 1
      if isinstance(separate, list):
          amount_canvases = len(separate) + 2
      elif separate is True:
          amount_canvases = len(dyn_peripheral[0]) + 2
  
      visualizer = dynamic_visualizer(
          amount_canvases, x_title="Time", y_title="V", y_labels=False
      )
      visualizer.append_dynamics(t, dyn_peripheral, 0, separate)
      visualizer.append_dynamics(
          t, dyn_central, amount_canvases - 2, True
      )
      visualizer.show()
  
file_path: pyclustering/pyclustering/nnet/examples/hhn_examples.py
imports: from pyclustering.nnet.dynamic_visualizer import dynamic_visualizer
  from pyclustering.nnet.hhn import hhn_network, hhn_parameters
label_desc: Use image node_id 1 for calling a global function with example usage: template_dynamic_hhn(num_osc, steps, time, stimulus, params, separate, ccore_flag) without return types