# A Predictive Coding Account for Chaotic Itinerancy

arXiv: https://arxiv.org/abs/2106.08937 · HTML: https://mlsanity.com/paper/2106.08937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [The free energy principle for action and perception: A mathematical review](https://mlsanity.com/paper/1705.09156/) (2017)
- [Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations](https://mlsanity.com/paper/1810.07411/) (2018)
- [Designing spontaneous behavioral switching via chaotic itinerancy](https://mlsanity.com/paper/2002.08332/) (2020)

## Built on by (most cited first, 1 of 1)

- [On the Relationship Between Variational Inference and Auto-Associative Memory](https://mlsanity.com/paper/2210.08013/) (2022)

## Abstract (arXiv)

As a phenomenon in dynamical systems allowing autonomous switching between stable behaviors, chaotic itinerancy has gained interest in neurorobotics research. In this study, we draw a connection between this phenomenon and the predictive coding theory by showing how a recurrent neural network implementing predictive coding can generate neural trajectories similar to chaotic itinerancy in the presence of input noise. We propose two scenarios generating random and past-independent attractor switching trajectories using our model.