A Deep Predictive Coding Network for Learning Latent Representations

TL;DR: An algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment is described and used to train a deep neural network on real-world images in a unsupervised learning paradigm.

Abstract: It has been argued that the brain is a prediction machine that continuously learns how to make better predictions about the stimuli received from the external environment It builds a model of the world around us and uses this model to infer the external stimulus Predictive coding has been proposed as a mechanism through which the brain might be able to build such a model of the external environment However, it is not clear how predictive coding can be used to build deep neural network models of the brain while complying with the architectural constraints imposed by the brain In this paper, we describe an algorithm to build a deep generative model using predictive coding that can be used to infer latent representations about the stimuli received from external environment Specifically, we used predictive coding to train a deep neural network on real-world images in a unsupervised learning paradigm To understand the capacity of the network with regards to modeling the external environment, we studied the latent representations generated by the model on images of objects that are never presented to the model during training Despite the novel features of these objects the model is able to infer the latent representations for them Furthermore, the reconstructions of the original images obtained from these latent representations preserve the important details of these objects