Maze navigation and memory with physical reservoir computers

The extent to which an organism’s morphology may shape its behaviour is increasingly studied, but still not well understood (McGeer, 1990; Pfeifer and Bongard, 2007; Nakajima et al., 2015; Caluwaerts et al., 2012; Zhao et al., 2013). Hauser et al. (2011, 2012) introduced mass-spring-damper (MSD) reservoir networks as morphologically computing abstracted bodies. As these networks are abstracted from biological bodies, the two will share some properties and capabilities, and studying the former may give us useful clues about the latter. We have previously applied small MSD network pairs to the production of reactive behaviour often referred to as ‘minimally cognitive’ (Johnson et al., 2014, 2015). Here we go on to use similar controllers to solve a target-seeking problem for a mobile agent in a maze, which necessitates memory, over a finite but extended period. If MSD networks with relatively few elements but still high dynamic complexity can solve navigation problems requiring this kind of short term memory, then we may speculate that simple organisms can also.