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About

C. J. A. Macleod’s research sits at the intersection of robotics, artificial intelligence, and unconventional computing, with a focus on how biological systems inspire novel control architectures. His major contributions center on **modular neural networks** and **artificial reaction networks**—a paradigm he helped pioneer that draws from the intelligent behavior of single-celled organisms (protoctists) rather than neurons. This work challenges traditional connectionist approaches, offering a different route to parallel distributed processing. Macleod has applied these frameworks to **legged robot locomotion**, particularly quadrupedal gaits, demonstrating adaptive dynamic control and evolutionary optimization. His most cited paper, “Incremental growth in modular neural networks” (2008), has garnered 10 citations, while his explorations of cell intelligence and temporal patterns in artificial reaction networks (2013, 2012) have each received 8 and 6 citations, respectively. Notably, his 2002 paper on a toad visual system-inspired neural network—integrating “What” and “Where” processing pathways—showcases his talent for translating biological principles into robotic perception. Though his citation counts are modest, Macleod’s work is conceptually bold, bridging robotics, evolutionary computation, and bio-inspired AI. His 2009 piece, “Minds for robots,” reflects a long-standing fascination with creating truly intelligent machines, a theme that runs through his career.