Efficient learning from explanation of prediction errors in streaming data

Streaming data from different kinds of sensors contributes to Big Data in a significant way. Recognizing the norms and abnormalities in such spatiotemporal data is a challenging problem. We present a general-purpose biologically-plausible computational model, called SELP, for learning the norms or invariances as features in an unsupervised and online manner from explanations of saliencies or surprises in the data. Given streaming data, this model runs a relentless cycle of Surprise → Explain → Learn → Predict involving the real external world and its internal model, and hence the name. The key characteristic of the model is its efficiency, crucial for streaming Big Data applications, which stems from two functionalities exploited at each sampling instant - it operates on the change in the state of data between consecutive sampling instants as opposed to the entire state of data, and it learns only from surprise or prediction error to update its internal state as opposed to learning from the entire input. The former allows the model to concentrate its computational resources on spatial regions of the data changing most frequently and ignore others, while the latter allows it to concentrate on those instants of time when its prediction is erroneous and ignore others. The model is implemented in a neural network architecture. We show the performance of the network in learning and retaining sequences of handwritten numerals. When exposed to natural videos acquired by a camera mounted on a cat´s head, the neurons learn receptive fields resembling simple cells in the primary visual cortex. The model leads to an agent-dependent framework for mining streaming data where the agent interprets and learns from the data in order to update its internal model.