In this paper, we present a novel robot model of touch, and its representation in an artificial cortex, that aims to capture some of the complexity of human touch. In particular, our approach integrates artificial mechanoception and nociception in an adaptive sensory field (the robot's "sensory body"), allowing for a more comprehensive simulation of tactile sensations. The robot's sensory field is then processed by a biologically plausible neural network in a way akin to sensory processing in the somatosensory and anterior cingulate cortex. Findings from our experimental results show our model's ability to integrate complex data from infrared sensors, leading to the emergence of a spatial sensory body representation in our neural network, with potentially significant implications for robot perception and interaction.