Mathematical models for cellular interactions in development. I. Filaments with one-sided inputs.

By A. Lindenmayer. 1968

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Deep learning in neural networks: An overview (2014, background): “…greatly reduce the search space by evolving compact encodings of NNs, e.g., through Lindenmeyer Systems (Jacob, Lindenmayer, & Rozenberg, 1994; Lindenmayer, 1968), graph rewriting (Kitano, 1990), Cellular Encoding (Gruau, Whitley, & Pyeatt, 1996), HyperNEAT (Clune, Stanley, Pennock, & Ofria,…”
Generative Art Using Neural Visual Grammars and Dual Encoders (2021, background): “The main ﬁnding of this work is that a hierarchical image generation system (which can be thought of as a neural Lindenmayer system [18]) can be evolved to produce a wide variety of structured images in conjunction with an image text dual encoder critic.”
GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops (2010, background): “, 2007) is a dynamic architecturalmodel ofwheat, based on the L-systemprinciples (Lindenmayer, 1968; Prusinkiewicz, 1999; Prusinkiewicz et al., 2000).”
3D Architectural Modelling of Aerial Photomorphogenesis in White Clover (Trifolium repens L.) using L-systems (2000, methodology): “…(Prusinkiewicz et al., 2000), which describes constituent elements of a compound module: M t; thM !d I t; thI K t; thK B t; thB 7 The square brackets indicate that the leaf primordium and the bud are positioned laterally (as branches) with respect to their supporting internode (Lindenmayer, 1968).”
In search of intelligence: evolving a developmental neuron capable of learning (2014, background): “Lindenmayer (1968) systems though introduced for modelling plant branching structures, its application to develop neural morphologies was also successful (Ascoli, Krichmar, Scorcioni, Nasuto, & Senft, 2001).”
Finite element tree crown hydrodynamics model (FETCH) using porous media flow within branching elements: A new representation of tree hydrodynamics (2005, methodology): “One of the popular models for branching-tree systems is based on the so-called L-system [Lindenmayer, 1968, 1971].”
Critical factors in the performance of hyperNEAT (2013, methodology): “Unlike previous indirect encoding methods, which build the phenotype temporally, in step-by-step fashion [1, 14, 19], HyperNEAT “captures the essential properties of natural developmental encoding without implementing a process of development” [21].”