Deep Predictive Coding Networks (DPCN)

The overall goal of the dynamical system at any layer is to make the best prediction of the representation in the layer below using the top-down information from the layers above and the temporal information from the previous states.

The brain is thought to seek to minimize value differences, and artificial networks are capable of both driving connections and conveying predictive information.

Computer models of predictive coding neuroscience can offer predictive capabilities and be classified into hierarchical deep neural networks. I think there is a very important feature of machine learning, namely the prerogative of a predictive neural network. Because of this characteristic, these networks are unable to perform effective incremental learning and are therefore unable to convey real predictable trust in the signal. The model is used to generate predictions of sensory input that are compared to actual sensory input. This comparison results in prediction errors that are then used to update and revise the mental model.

An artificial neural network is a connected group of nodes inspired by the simplification of neurons in the brain. While the logistic sigmoid has a nice biological interpretation, it turns out that some neural networks get stuck in training.

The overall goal of the dynamical system at any layer is to make the best prediction of the representation in the layer below using the top-down information from the layers above and the temporal information from the previous states. That is why it is named the name deep predictive coding networks (DPCN).

PredNet is a deep convolutional recurring neural network inspired by the human brain’s neural networks, such as the Deep Neural Network (DNN). Its architecture is illustrated in figure 1. PredNet learn to predict future frames of a video sequence through a layer of the network by making local predictions using backward information from the top layer and passing only differential or…