def get_phenotype_size(self):
     """
     Return the dimensions required to produce a phenotype.
     Cross-layer connections are NxM matrices.
     Inter-layer connections are NxN matrices.
     Also returns the number of regular neurons that need to be assigned t and gain values.
     """
     return {
         'cross': [
             (sum(self.neuron_count(i)), self.neuron_count(i + 1)[0]) for i in xrange(len(self.layers) - 1)
         ],
         'inter': [
             tuplize(self.neuron_count(i)[0]) for i in xrange(1, len(self.layers))
         ],
         'neurons': sum(self.neuron_count(i)[0] for i in xrange(len(self.layers)))
     }

 def setContributors( self, contributors ):
     """ Set Dublin Core Contributor elements - resource collaborators.
     """
     # XXX: fixme
     self.contributors = tuplize('contributors', contributors, semi_split)

 def setSubject( self, subject ):
     """ Set Dublin Core Subject element - resource keywords.
     """
     self.subject = tuplize( 'subject', subject )

 def setCreators(self, creators):
     """ Set Dublin Core Creator elements - resource authors.
     """
     self.creators = tuplize('creators', creators)

 def setContributors( self, contributors ):
     "Dublin Core element - additional contributors to resource"
     # XXX: fixme
     self.contributors = tuplize( 'contributors', contributors )

 def setSubject( self, subject ):
     "Dublin Core element - resource keywords"
     self.subject = tuplize( 'subject', subject )