There are no explicit equations for the cerebellum traditionally also has access to a desired state ds (in particular, one can consider this a and forward DNI, respectively; L denotes the loss function. In addition, the inverse model of the of a motor area and sensory area, respectively; CB,CF denotes the computation of backward DNI Notation is largely consistent with section 2 of the main text: hm,hs denotes the hidden activity properties of the inverse model of the cerebellum can be set against those of forward DNI (red). Controller Neocortex Main model Cerebellum Synthesiser Forward Model Backward DNIInverse Model Forward DNI be summarised in table S1. In general, the likeness in formulation between DNI and the cerebellar internal model hypothesis can backward DNI where the main model is an motor-associated RNN. In fact, it was recently suggested that the cerebellum out that though the temporal case of forward DNI was not originally considered in [5], there remain learns to mimic the forward computations which then take place in the neocortex.

Self-supervised learning (SSL) methods aim to exploit the abundance of unlabelled data for machine learning (ML), however the underlying principles are often method-specific. An SSL framework derived from biological first principles of embodied learning could unify the various SSL methods, help elucidate learning in the brain, and possibly improve ML. SSL commonly transforms each training datapoint into a pair of views, uses the knowledge of this pairing as a positive (i.e.

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We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics. Our model is evaluated on a real-world robotic manipulation task that requires displacing objects to target locations by poking. The robot gathered over 400 hours of experience by executing more than 50K pokes on different objects. We propose a novel approach based on deep neural networks for modeling the dynamics of robot's interactions directly from images, by jointly estimating forward and inverse models of dynamics. The inverse model objective provides supervision to construct informative visual features, which the forward model can then predict and in turn regularize the feature space for the inverse model. The interplay between these two objectives creates useful, accurate models that can then be used for multi-step decision making. This formulation has the additional benefit that it is possible to learn forward models in an abstract feature space and thus alleviate the need of predicting pixels. Our experiments show that this joint modeling approach outperforms alternative methods. We also demonstrate that active data collection using the learned model further improves performance.

We propose information gating as a way to learn parsimonious representations that identify the minimal information required for a task.

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Neural Information Processing Systems http://nips.cc/