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About

Tom Smith is a pioneering researcher in evolutionary robotics and bio-inspired artificial neural networks, whose work has fundamentally shaped how we design adaptive robot controllers. His primary research areas include neuromodulatory neural networks, evolutionary computation, and the study of fitness landscapes in robotics. Smith's most significant contribution is the development of GasNets—a novel class of artificial neural networks inspired by the diffusive signaling of gaseous neurotransmitters like nitric oxide. His seminal 1998 paper, "Better Living Through Chemistry," with 152 citations, demonstrated that GasNets are more evolvable than traditional ANNs, enabling robots to solve complex tasks through evolutionary optimization. He further explored the role of neutral evolution in robotic search spaces, showing how statistical neutrality enhances evolvability, as seen in his 2002 paper on neutral networks (49 citations). Smith's work on temporal adaptivity and neuronal plasticity, detailed in his 2002 analysis (41 citations), provided crucial insights into the functional operation of evolved controllers. Beyond foundational theory, he contributed to practical robotics with the ACROBOTER platform (2009, 23 citations), a ceiling-based service robot. His research, spanning over two decades, has garnered over 400 citations, establishing him as a key figure in bridging biological principles with artificial intelligence.