Emergence of self-organized amoeboid movement in a multi-agent approximation of Physarum polycephalum

The giant single-celled slime mould Physarum polycephalum exhibits complex morphological adaptation and amoeboid movement as it forages for food and may be seen as a minimal example of complex robotic behaviour. Swarm computation has previously been used to explore how spatio-temporal complexity can emerge from, and be distributed within, simple component parts and their interactions. Using a particle-based swarm approach we explore the question of how to generate collective amoeboid movement from simple non-oscillatory component parts in a model of P. polycephalum. The model collective behaves as a cohesive and deformable virtual material, approximating the local coupling within the plasmodium matrix. The collective generates de-novo and complex oscillatory patterns from simple local interactions. The origin of this motor behaviour distributed within the collective rendering is morphologically adaptive, amenable to external influence and robust to simulated environmental insult. We show how to gain external influence over the collective movement by simulated chemo-attraction (pulling towards nutrient stimuli) and simulated light irradiation hazards (pushing from stimuli). The amorphous and distributed properties of the collective are demonstrated by cleaving it into two independent entities and fusing two separate entities to form a single device, thus enabling it to traverse narrow, separate or tortuous paths. We conclude by summarizing the contribution of the model to swarm-based robotics and soft-bodied modular robotics and discuss the future potential of such material approaches to the field. © 2012 IOP Publishing Ltd

Allorecognition behavior of slime mold plasmodium—Physarum rigidum slime sheath-mediated self-extension model

When allogeneic plasmodia of Myxomycetes encounter each other, they fuse or avoid each other depending on the combination, indicating that plasmodia can recognize self and non-self. The mechanisms of allorecognition are not fully understood from the perspective of behavior. In the present study design, Physarum rigidum geographical strains naturally encountered each other with the aim of formulating a model of allorecognition behavior. The plasmodia of P. rigidum can recognize each other by touching the cell membrane surface. However, contact is not necessary. Cases involving the absence of contact occur by the slime sheath of hyaline mucus that covers plasmodium. This so-called non-contact allorecognition has distinct characteristics regardless of distance and is faster compared to that involving contact. These facts suggest that the plasmodia of one P. rigidum can recognize others and can rapidly and safely decide whether to avoid or fuse with other plasmodia, using the non-contact allorecognition. Previous studies on P. polycephalum have regarded the slime sheath as a repellent or as an external memory for self. These studies advocated that the principle of the plasmodium is basically avoidance of other individuals. In this study, we propose the self-extension model based on data of the allorecognition behavior of P. rigidum. According to the model, the slime sheath functions as a signal that disperses information about self into the environment. Self-extension by the slime sheath enables non-contact allorecognition

Towards a Physarum learning chip

Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants and behaviour of the slime mould is tuned by a range of repellents, the organism preforms computation. It maps given data configuration into a protoplasmic network. To discover physical means of programming the slime mould computers we explore conductivity of the protoplasmic tubes; proposing that the network connectivity of protoplasmic tubes shows pathway-dependent plasticity. To demonstrate this we encourage the slime mould to span a grid of electrodes and apply AC stimuli to the network. Learning and weighted connections within a grid of electrodes is produced using negative and positive voltage stimulation of the network at desired nodes; low frequency (10 Hz) sinusoidal (0.5 V peak-to-peak) voltage increases connectivity between stimulated electrodes while decreasing connectivity elsewhere, high frequency (1000 Hz) sinusoidal (2.5 V peak-to-peak) voltage stimulation decreases network connectivity between stimulated electrodes. We corroborate in a particle model. This phenomenon may be used for computation in the same way that neural networks process information and has the potential to shed light on the dynamics of learning and information processing in non-neural metazoan somatic cell networks