Learning a Peripersonal Space Representation as a Visuo-Tactile Prediction Task
The space immediately surrounding our body, or peripersonal space, is crucial for interaction with the environment. In primate brains, specific neural circuitry is responsible for its encoding. An important component is a safety margin around the body that draws on visuo-tactile interactions: approaching stimuli are registered by vision and processed, producing anticipation or prediction of contact in the tactile modality. The mechanisms of this representation and its development are not understood. We propose a computational model that addresses this: a neural network composed of a Restricted Boltzmann Machine and a feedforward neural network. The former learns in an unsupervised manner to represent position and velocity features of the stimulus. The latter is trained in a supervised way to predict the position of touch (contact). Unique to this model, it considers: (i) stimulus position and velocity, (ii) uncertainty of all variables, and (iii) not only multisensory integration but also prediction.
KeywordsPeripersonal space Touch RBM Probabilistic population code Visuo-tactile integration
Z.S. was supported by The Grant Agency of the CTU Prague project SGS16/161/ OHK3/2T/13. M.H. was supported by the Czech Science Foundation under Project GA17-15697Y and a Marie Curie Intra European Fellowship (iCub Body Schema 625727) within the 7th European Community Framework Programme. Base code for the RBM model was kindly provided by Joseph G. Makin .
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