You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: Recent neurophysiological experiments appear to indicate that the responses of visual cortical neurons in a monkey freely viewing a natural scene can sometimes differ substantially from those obtained when the same image subregions are flashed during a conventional fixation task. These new findings attain significance from the fact that neurophysiological research in the past has been based predominantly on cell recordings obtained during fixation tasks, under the assumption that these data would be useful in predicting responses in more general situations. We describe a hierarchical model of visual memory that reconciles the two differing experimental results mentioned above by predicting neural responses in both fixating and free-viewing conditions. The model dynamically combines input-driven bottom-up signals with expectation-driven top-down signals to achieve optimal estimation of current state using a Kalman filter based framework. The architecture of the model posits a role for the reciprocal connections between adjoining visual cortical areas in determining neural response properties.
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 [1] 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.