Problem:
 g(X) -> h(activate(X))
 c() -> d()
 h(n__d()) -> g(n__c())
 d() -> n__d()
 c() -> n__c()
 activate(n__d()) -> d()
 activate(n__c()) -> c()
 activate(X) -> X

Proof:
 Matrix Interpretation Processor: dim=3
  
  interpretation:
            [0]
   [n__c] = [1]
            [0],
   
            [0]
   [n__d] = [0]
            [1],
   
         [0]
   [d] = [0]
         [1],
   
         [0]
   [c] = [1]
         [1],
   
             [1 0 0]     [1]
   [h](x0) = [0 0 0]x0 + [1]
             [0 0 0]     [0],
   
                    [1 0 1]  
   [activate](x0) = [0 1 0]x0
                    [0 1 1]  ,
   
             [1 0 1]     [1]
   [g](x0) = [1 0 0]x0 + [1]
             [1 0 1]     [0]
  orientation:
          [1 0 1]    [1]    [1 0 1]    [1]                 
   g(X) = [1 0 0]X + [1] >= [0 0 0]X + [1] = h(activate(X))
          [1 0 1]    [0]    [0 0 0]    [0]                 
   
         [0]    [0]      
   c() = [1] >= [0] = d()
         [1]    [1]      
   
               [1]    [1]            
   h(n__d()) = [1] >= [1] = g(n__c())
               [0]    [0]            
   
         [0]    [0]         
   d() = [0] >= [0] = n__d()
         [1]    [1]         
   
         [0]    [0]         
   c() = [1] >= [1] = n__c()
         [1]    [0]         
   
                      [1]    [0]      
   activate(n__d()) = [0] >= [0] = d()
                      [1]    [1]      
   
                      [0]    [0]      
   activate(n__c()) = [1] >= [1] = c()
                      [1]    [1]      
   
                 [1 0 1]          
   activate(X) = [0 1 0]X >= X = X
                 [0 1 1]          
  problem:
   g(X) -> h(activate(X))
   c() -> d()
   h(n__d()) -> g(n__c())
   d() -> n__d()
   c() -> n__c()
   activate(n__c()) -> c()
   activate(X) -> X
  Matrix Interpretation Processor: dim=3
   
   interpretation:
             [0]
    [n__c] = [1]
             [0],
    
             [1]
    [n__d] = [0]
             [0],
    
          [1]
    [d] = [0]
          [0],
    
          [1]
    [c] = [1]
          [0],
    
              [1 0 1]  
    [h](x0) = [1 0 0]x0
              [0 0 0]  ,
    
                     [1 1 0]  
    [activate](x0) = [1 1 0]x0
                     [0 0 1]  ,
    
              [1 1 1]  
    [g](x0) = [1 1 0]x0
              [0 0 1]  
   orientation:
           [1 1 1]     [1 1 1]                  
    g(X) = [1 1 0]X >= [1 1 0]X = h(activate(X))
           [0 0 1]     [0 0 0]                  
    
          [1]    [1]      
    c() = [1] >= [0] = d()
          [0]    [0]      
    
                [1]    [1]            
    h(n__d()) = [1] >= [1] = g(n__c())
                [0]    [0]            
    
          [1]    [1]         
    d() = [0] >= [0] = n__d()
          [0]    [0]         
    
          [1]    [0]         
    c() = [1] >= [1] = n__c()
          [0]    [0]         
    
                       [1]    [1]      
    activate(n__c()) = [1] >= [1] = c()
                       [0]    [0]      
    
                  [1 1 0]          
    activate(X) = [1 1 0]X >= X = X
                  [0 0 1]          
   problem:
    g(X) -> h(activate(X))
    c() -> d()
    h(n__d()) -> g(n__c())
    d() -> n__d()
    activate(n__c()) -> c()
    activate(X) -> X
   Matrix Interpretation Processor: dim=3
    
    interpretation:
              [0]
     [n__c] = [0]
              [0],
     
              [1]
     [n__d] = [0]
              [0],
     
           [1]
     [d] = [1]
           [0],
     
           [1]
     [c] = [1]
           [0],
     
               [1 0 1]     [0]
     [h](x0) = [0 0 0]x0 + [1]
               [0 0 0]     [0],
     
                      [1 1 0]     [1]
     [activate](x0) = [1 1 0]x0 + [1]
                      [0 0 1]     [0],
     
               [1 1 1]     [1]
     [g](x0) = [0 0 1]x0 + [1]
               [0 0 0]     [0]
    orientation:
            [1 1 1]    [1]    [1 1 1]    [1]                 
     g(X) = [0 0 1]X + [1] >= [0 0 0]X + [1] = h(activate(X))
            [0 0 0]    [0]    [0 0 0]    [0]                 
     
           [1]    [1]      
     c() = [1] >= [1] = d()
           [0]    [0]      
     
                 [1]    [1]            
     h(n__d()) = [1] >= [1] = g(n__c())
                 [0]    [0]            
     
           [1]    [1]         
     d() = [1] >= [0] = n__d()
           [0]    [0]         
     
                        [1]    [1]      
     activate(n__c()) = [1] >= [1] = c()
                        [0]    [0]      
     
                   [1 1 0]    [1]         
     activate(X) = [1 1 0]X + [1] >= X = X
                   [0 0 1]    [0]         
    problem:
     g(X) -> h(activate(X))
     c() -> d()
     h(n__d()) -> g(n__c())
     d() -> n__d()
     activate(n__c()) -> c()
    Unfolding Processor:
     loop length: 5
     terms:
      g(n__c())
      h(activate(n__c()))
      h(c())
      h(d())
      h(n__d())
     context: []
     substitution:
      
     Qed