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Extracting and binding salient information from different sensory modalitiesto determine common features in the environment is a significant challenge inrobotics. Here we present MuPNet (Multi-modal Predictive Coding Network), abiologically plausible network architecture for extracting joint latentfeatures from visuo-tactile sensory data gathered from a biomimetic mobilerobot. In this study we evaluate MuPNet applied to place recognition as asimulated biomimetic robot platform explores visually aliased environments. TheF1 scores demonstrate that its performance over prior hand-crafted sensoryfeature extraction techniques is equivalent under controlled conditions, withsignificant improvement when operating in novel environments.

MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations