Fractals are illustrious for their repetitive patterning in limited space that create dynamic and complex geometric shapes. Despite this complexity the mathematical solutions to generate fractals are often simple compared to their final products. In biology, the brain shares reverence with fractals for its degree of structural complexity that shapes our cognition and behavior. In the same vein as fractals, the brain forms through repetitive patterning of comparatively simple pathways regulated by a limited space of genetic factors. In the following thesis, I explore how these genetic factors shape the brain’s structure resulting in distinct computational properties that enable the brain’s functionality. Using RNA centric toolkits I identified a non-coding RNA, Ganon-1, that is enriched in cortical neuron growth cones during development. Ganon-1 RNA is developmentally regulated with peak expression around post-natal day 3 (PN3) that declines later in life. RNAscope, a technique that using gene specific approaches to label RNA, paired with immunohistochemistry suggests that Ganon-1 is primarily expressed in long range projection neurons throughout the brain having unique topographic expression across the cortical-striatal circuitry. Sequencing of Ganon-1 using gene specific approaches reveal RNA motifs that associate Ganon-1 with the mTOR complex. RNA Co-immunoprecipitation using mTOR antibodies showed increased isolation of Ganon-1 in mTOR isolated samples suggesting Ganon-1 does bind with mTOR. Overexpressing Ganon-1 in cultured cortical neurons resulted in longer neurite outgrowth after day in vitro (DIV) 4. In later chapters, I explore functional components of the cortical-striatal circuitry through computational modeling. I find that a probabilistic neural network modeled after the cortical-striatal circuitry generates synchrony through convergent cortical input rather than gap-junction lateral connectivity within the striatum. In chapter three I build upon this computational model by exploring the developmental context of lateral connectivity and show that lateral connections improve the probabilistic neural network’s ability to generalize information. My thesis builds the foundation for the fractal brain analogy by identifying novel genetic factors that shape neural development and brain formation that influence the computational prowess of the brain.