anchor.diff:  
   [.text-center]
   .Training convergence graphs of loss and accuracy on validation set
  -image::sim_neuron/convergence.png[convergence_graphs]
  +image::sim_neuron/convergence.png[convergence_graphs, 600]
   {empty} +
   
   TODO: Compare learned conv filters between old/new model.
  
positive.code: 
  [.text-center]
  .Test model
  image::sim_neuron/model.png[test_model]
  {empty} +
  
  == Results
  
negative.code: 
  [.text-center]
  .Computation model of a neuron
  image::sim_neuron/neuron_model.jpeg[neuron_model, 400]
  {empty} +
  
  All architectures build on this fundamental computational model. Let us examine what the computation is conceptually doing in a step by step fashion.
  