# Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model

arXiv: https://arxiv.org/abs/1706.10240 · HTML: https://mlsanity.com/paper/1706.10240/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2017.

## Built on by (most cited first, 3 of 3)

- [Goal-Directed Planning by Reinforcement Learning and Active Inference](https://mlsanity.com/paper/2106.09938/) (2021)
- [Predictive coding, precision and natural gradients](https://mlsanity.com/paper/2111.06942/) (2021)
- [Improving the Level of Autism Discrimination through GraphRNN Link Prediction](https://mlsanity.com/paper/2202.09538/) (2022)

## Abstract (arXiv)

The current paper proposes a novel variational Bayes predictive coding RNN model, which can learn to generate fluctuated temporal patterns from exemplars. The model learns to maximize the lower bound of the weighted sum of the regularization and reconstruction error terms. We examined how this weighting can affect development of different types of information processing while learning fluctuated temporal patterns. Simulation results show that strong weighting of the reconstruction term causes the development of deterministic chaos for imitating the randomness observed in target sequences, while strong weighting of the regularization term causes the development of stochastic dynamics imitating probabilistic processes observed in targets. Moreover, results indicate that the most generalized learning emerges between these two extremes. The paper concludes with implications in terms of the underlying neuronal mechanisms for autism spectrum disorder and for free action.