Abstract
Neural network morphology in Artificial Neural Networks (ANN) is typically designed depending on specific learning purposes. Biological neural networks, on the contrary, generate their morphology using biochemical markers secreted by each neuron. Specific features such as molecular signalling, electrochemical alphabet and neurite propagation rules are genetically encoded. However, the environment plays also a critical role in network morphology. Neurites are propagated through tissues to reach target neurons, following paths defined by the diffusion of molecular markers. Neurite paths are affected among other phenomena by competence for synaptic resources and volumetric economy.
Along this paper we observe some of the mechanisms of biological morphogenesis and their mathematical models. We analyze neurite navigation in short distances using local random propagation rules. Then, using reaction-difussion patterns, the process of molecular signalling and its influence in network morphology is studied. Finally we combine both strategies to generate morphology in ANN’s.
J. Gomez Perdomo—Universidad Nacional de Colombia. ALIFE research group.
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The authors wish to thank ALIFE research group at the Universidad Nacional de Colombia for their support in developing this research.
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Espinosa Garcia, A., Gomez Perdomo, J. (2015). A Simple Bio-Inspired Model for Synaptogenesis in Artificial Neural Networks. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_24
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DOI: https://doi.org/10.1007/978-3-319-27060-9_24
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