class_docstr: !
  @brief Parameters for pulse coupled neural network.
class_docstr_tok: ["!","@","brief","Parameters","for","pulse","coupled","neural","network","."]
docstr: !
  @brief    Default constructor of parameters for pulse-coupled neural network.
  @details  Constructor initializes parameters by default non-zero values that can be
            used for simple simulation.
docstr_tok: ["!","@","brief","Default","constructor","of","parameters","for","pulse-coupled","neural","network",".","@","details","Constructor","initializes","parameters","by","default","non-zero","values","that","can","be","used","for","simple","simulation","."]
code: def __init__(self):
      """!
      @brief    Default constructor of parameters for pulse-coupled neural network.
      @details  Constructor initializes parameters by default non-zero values that can be
                used for simple simulation.
      """
  
      ## Multiplier for the feeding compartment at the current step.
      self.VF = 1.0
  
      ## Multiplier for the linking compartment at the current step.
      self.VL = 1.0
  
      ## Multiplier for the threshold at the current step.
      self.VT = 10.0
  
      ## Multiplier for the feeding compartment at the previous step.
      self.AF = 0.1
  
      ## Multiplier for the linking compartment at the previous step.
      self.AL = 0.1
  
      ## Multiplier for the threshold at the previous step.
      self.AT = 0.5
  
      ## Synaptic weight - neighbours influence on linking compartment.
      self.W = 1.0
  
      ## Synaptic weight - neighbours influence on feeding compartment.
      self.M = 1.0
  
      ## Linking strength in the network.
      self.B = 0.1
  
      ## Enable/disable Fast-Linking mode. Fast linking helps to overcome some of the effects of time quantisation. This process allows the linking wave to progress a lot faster than the feeding wave.
      self.FAST_LINKING = False
  
imports: import random
  import numpy
  import matplotlib.pyplot
  import matplotlib.animation
  from PIL import Image
  from pyclustering.nnet import *
  from pyclustering.core.wrapper import ccore_library
  import pyclustering.core.pcnn_wrapper
  from pyclustering.utils import draw_dynamics
label_desc: Use image node_id 1 to create a new pcnn_parameters object  with example: obj = pcnn_parameters()