Supplementing evolutionary developmental systems with abstract models of neurogenesis

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Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Downing (2007) looks at different computational models of neurogenesis to propose an evolutionary developmental system, focusing in particular on abstraction levels and principles such as Neural Darwinism (Edelman and Tononi, 2000).”
Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture (2011, background): “Downing favors a higher abstraction level in neural development to avoid the complexities of axonal and dendritic growth while maintaining key aspects of cell signaling, competition, and cooperation of neural topologies in nature (Downing, 2007).”
In search of intelligence: evolving a developmental neuron capable of learning (2014, background): “Downing (2007) chose a higher abstraction level in his neural developmental system, avoiding the complexities of axonal and dendritic growth and retaining key aspects of cell signalling, competition and cooperation of neural topologies in nature.”