We explore whether useful temporal neural generative models can be learned from sequential data without back-propagation through time. We investigate the viability of a more neurocognitively-grounded approach in the context of unsupervised generative modeling of sequences. Specifically, we build on the concept of predictive coding, which has gained influence in cognitive science, in a neural framework. To do so we develop a novel architecture, the Temporal Neural Coding Network, and its learning algorithm, Discrepancy Reduction. The underlying directed generative model is fully recurrent, meaning that it employs structural feedback connections and temporal feedback connections, yielding information propagation cycles that create local learning signals. This facilitates a unified bottom-up and top-down approach for information transfer inside the architecture. Our proposed algorithm shows promise on the bouncing balls generative modeling problem. Further experiments could be conducted to explore the strengths and weaknesses of our approach.
Predictive coding theories posit that the brain is in a continuous process of creating and updating hypotheses that predict the sensory input it receives, directly influencing conscious experience . Models of sparse coding and predictive coding embody the idea that the brain is a directed generative model where the processes of generation (top-down mechanisms) and inference (bottom-up mechanisms) are intertwined and interact to perform a sort of iterative inference of latent variables states. Furthermore, nesting the ideas of predictive coding within the Kalman Filter framework @cite_7 can create dynamic models that handle time-varying data. Many variations and implementations of predictive coding have been developed . Some approaches, such as predictive sparse decomposition , attempt to speed up the iterative inference by introducing an inference network, but this again, creates a problem similar to that of variational inference--the generative model is constrained by the quality of the approximate inference model.
@cite_7
2030802915
Predictive coding posits that neural systems make forward-looking predictions about incoming information. Neural signals contain information not about the currently perceived stimulus, but about the difference between the observed and the predicted stimulus. We propose to extend the predictive coding framework from high-level sensory processing to the more abstract domain of theory of mind; that is, to inferences about others' goals, thoughts, and personalities. We review evidence that, across brain regions, neural responses to depictions of human behavior, from biological motion to trait descriptions, exhibit a key signature of predictive coding: reduced activity to predictable stimuli. We discuss how future experiments could distinguish predictive coding from alternative explanations of this response profile. This framework may provide an important new window on the neural computations underlying theory of mind.