2014/05/13 by Ana M. Mihut, Mihut, Ana M., Graham Morgan +3
Biochemistry, Genetics and Molecular Biology · Engineering · Neuroscience · #3D Printing in Biomedical Research #Axon Guidance and Neuronal Signaling #Cell Image Analysis Techniques #FOS: Biological sciences #Neurogenesis and neuroplasticity mechanisms #Neurons and Cognition (q-bio.NC) #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1405.3331
Dynamic Connectome Lab, Technical Report 2
arxiv created 2014/05/13 · openalex publication_date 2014/05/13 · arxiv updated 2014/05/15 · openalex created_date 2022/08/30 · openalex updated_date 2026/07/28
Neural development represents not only an exciting and complex field of study, with ongoing progress, but it also became the epicentre of neuroscience and developmental biology, as it strives to describe the underlying cellular and molecular mechanisms by which the central nervous system emerges during the various levels of embryonic development phases. The nervous system is a dynamic entity, where the genetic information plays an important role in shaping the intra- and extracellular environments, which in turn offer a reliable foundation for the stem cell precursors to divide and form neurons. Throughout the embryonic development stages, the neurons undergo different processes: migration at an immature level from the initial place in the embryo to a predefined final position, axonal differentiation and guidance of the motile growth cone towards a postsynaptic target, synaptic formation between axons and target, and lastly long-term synaptic changes which underlie learning and memory. In order to gain a better understanding of how the nervous system develops, mathematical and computational models have been created and expanded in order to bridge the gap between system-level dynamics and lower level cellular and molecular processes. This research paper aims to illustrate the potential of theoretical mathematical and computational models for analysing one important stage of neural development - axonal growth and guidance mechanisms in the presence of diffusion cues, through a visual simulation which is optimized via the graphic processing unit and parallel programming techniques.