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Training sparse convolutional deep predictive coding networks with attention.

Hongming Li¹, Chi Ding¹, José C Príncipe¹

¹Computational NeuroEngineering Laboratory, University of Florida, Gainesville, 32611, FL, US.

Neural Networks : the Official Journal of the International Neural Network Society

|March 20, 2026

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This study introduces a new self-supervised learning method for sparse deep predictive coding networks with attention (DPCN-SCA). The novel approach enhances feature learning and classification accuracy, outperforming existing unsupervised methods.

Area of Science:

Artificial Intelligence
Machine Learning
Computer Vision

Background:

Deep predictive coding networks (DPCNs) are explored for unsupervised feature learning.
Self-supervised learning (SSL) methods leverage data structure without explicit labels.
Attention mechanisms enhance model focus on relevant information.

Purpose of the Study:

To propose a novel training methodology for sparse convolutional deep predictive coding networks with attention (DPCN-SCA).
To adapt DPCN architecture for visual memory and improved feature interpretability.
To evaluate the performance of DPCN-SCA on benchmark datasets.

Main Methods:

Modified traditional DPCN equations to incorporate a top-down flow, inspired by autoencoders.
Employed a bidirectional architecture and attention mechanisms within a sparse convolutional framework.

Keywords:

Autoencoder Brain-inspired learning Deep predictive coding network Sparse coding

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Utilized benchmark datasets to validate the DPCN-SCA model, similar in depth to AlexNet.

Main Results:

DPCN-SCA demonstrated feature extraction patterns similar to supervised CNNs, focusing on details in early layers and contours in deeper layers.
Achieved over a 20% absolute gain in classification accuracy compared to unsupervised sparse coding baselines at 1-5% sparsity levels.
Introduced a new visualization technique to observe attention patterns in deep layers by projecting activations back to the input space.

Conclusions:

The proposed DPCN-SCA training methodology is effective for unsupervised feature learning and classification.
The adapted DPCN architecture offers benefits for visual memory and interpretability.
DPCN-SCA significantly advances unsupervised sparse coding performance, particularly under high sparsity constraints.