A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems

By Moshe Sipper, Eduardo Sanchez, D. Mange, Marco Tomassini, A. Pérez-Uribe, A. Stauffer. 1997

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Thus, it is no surprise that scientists have tried to engineer artificial systems to reproduce such phenomena (Sanchez et al., 1996; Sipper et al., 1997; Dawkins, 2003).”
Metaheuristics in combinatorial optimization: Overview and conceptual comparison (2003, background): “Recent successes were obtained in the rapidly growing bioinformatics area (see, e.g., Fogel et al. [2002]), but also in multi-objective optimization [Coello Coello 2000], and in evolvable hardware [Sipper et al. 1997].”
Structure-Adaptable Digital Neural Networks (1999, background): “In particular, researchers at the Logic Systems Laboratory have used eld-programmable logic devices (FPGAs) to implement bio-inspired hardware systems, capable of evolution, growth, and self-repair [130], along all the three axes of the POE model [193].”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, background): “2: Phylogenetic, Ontogenetic and Epigenetic space (POE) also referred to as Evolution, Development and Learning space (Sipper et al., 1997).”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “In analogy to nature, bio-inspired hardware systems can be classified as three axes, phylogeny, ontogeny, and epigenesis [171].”
Advances in Computational Intelligence (2003, unknown): “A few approaches to the classification of evolvable hardware have been reported [165, 172, 202, 227, 229].”