# Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics

arXiv: https://arxiv.org/abs/2106.02749 · HTML: https://mlsanity.com/paper/2106.02749/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 78 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [HopSkipJumpAttack: A Query-Efficient Decision-Based Attack](https://mlsanity.com/paper/1904.02144/) (2019)
- [Adversarially Robust Generalization Requires More Data](https://mlsanity.com/paper/1804.11285/) (2018)
- [Feature Denoising for Improving Adversarial Robustness](https://mlsanity.com/paper/1812.03411/) (2018)
- [Adversarial Examples Improve Image Recognition](https://mlsanity.com/paper/1911.09665/) (2019)
- [MagNet: a Two-Pronged Defense against Adversarial Examples](https://mlsanity.com/paper/1705.09064/) (2017)
- [PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples](https://mlsanity.com/paper/1710.10766/) (2017)
- [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning](https://mlsanity.com/paper/1605.08104/) (2016)
- [Learning long-range spatial dependencies with horizontal gated-recurrent units](https://mlsanity.com/paper/1805.08315/) (2018)
- [Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training](https://mlsanity.com/paper/1907.10764/) (2019)
- [Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs](https://mlsanity.com/paper/1909.06161/) (2019)
- [The Odds are Odd: A Statistical Test for Detecting Adversarial Examples](https://mlsanity.com/paper/1902.04818/) (2019)
- [A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples](https://mlsanity.com/paper/1608.07690/) (2016)
- [Recurrence is required to capture the representational dynamics of the human visual system](https://mlsanity.com/paper/1903.05946/) (2019)
- [APE-GAN: Adversarial Perturbation Elimination with GAN](https://mlsanity.com/paper/1707.05474/) (2017)
- [Dense Associative Memory is Robust to Adversarial Inputs](https://mlsanity.com/paper/1701.00939/) (2017)
- [Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples](https://mlsanity.com/paper/1810.11580/) (2018)
- [The Robust Manifold Defense: Adversarial Training using Generative Models](https://mlsanity.com/paper/1712.09196/) (2017)
- [Deep Predictive Coding Network for Object Recognition](https://mlsanity.com/paper/1802.04762/) (2018)
- [Neural Networks with Recurrent Generative Feedback](https://mlsanity.com/paper/2007.09200/) (2020)
- [Adversarial Robustness of Supervised Sparse Coding](https://mlsanity.com/paper/2010.12088/) (2020)

## Built on by (most cited first, 19 of 19)

- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks](https://mlsanity.com/paper/2212.00720/) (2022)
- [Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images](https://mlsanity.com/paper/2211.12047/) (2022)
- [Diversity vs. Recognizability: Human-like generalization in one-shot generative models](https://mlsanity.com/paper/2205.10370/) (2022)
- [Predictive Coding Networks and Inference Learning: Tutorial and Survey](https://mlsanity.com/paper/2407.04117/) (2024)
- [Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning](https://mlsanity.com/paper/2403.07078/) (2024)
- [Multimodal neural networks better explain multivoxel patterns in the hippocampus](https://mlsanity.com/paper/2201.11517/) (2021)
- [Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks](https://mlsanity.com/paper/2406.06079/) (2024)
- [On the role of feedback in visual processing: a predictive coding perspective](https://mlsanity.com/paper/2106.04225/) (2021)
- [Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment](https://mlsanity.com/paper/2310.20599/) (2023)
- [pyhgf: A neural network library for predictive coding](https://mlsanity.com/paper/2410.09206/) (2024)
- [An overview of open source Deep Learning-based libraries for Neuroscience](https://mlsanity.com/paper/2301.05057/) (2022)
- [Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG](https://mlsanity.com/paper/2409.03646/) (2024)
- [Contextual Feedback Loops: Amplifying Deep Reasoning with Iterative Top-Down Feedback](https://mlsanity.com/paper/2412.17737/) (2024)
- [Fixed Inter-Neuron Covariability Induces Adversarial Robustness](https://mlsanity.com/paper/2308.03956/) (2023)
- [Early Exiting Predictive Coding Neural Networks for Edge AI](https://mlsanity.com/paper/2309.02022/) (2023)
- [How Robust Are Energy-Based Models Trained With Equilibrium Propagation?](https://mlsanity.com/paper/2401.11543/) (2024)
- [GASPnet: Global Agreement to Synchronize Phases](https://mlsanity.com/paper/2507.16674/) (2025)
- [Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models](https://mlsanity.com/paper/2508.07115/) (2025)

## Abstract (arXiv)

Deep neural networks excel at image classification, but their performance is far less robust to input perturbations than human perception. In this work we explore whether this shortcoming may be partly addressed by incorporating brain-inspired recurrent dynamics in deep convolutional networks. We take inspiration from a popular framework in neuroscience: 'predictive coding'. At each layer of the hierarchical model, generative feedback 'predicts' (i.e., reconstructs) the pattern of activity in the previous layer. The reconstruction errors are used to iteratively update the network's representations across timesteps, and to optimize the network's feedback weights over the natural image dataset-a form of unsupervised training. We show that implementing this strategy into two popular networks, VGG16 and EfficientNetB0, improves their robustness against various corruptions and adversarial attacks. We hypothesize that other feedforward networks could similarly benefit from the proposed framework. To promote research in this direction, we provide an open-sourced PyTorch-based package called Predify, which can be used to implement and investigate the impacts of the predictive coding dynamics in any convolutional neural network.