Efficient Online Learning with Predictive Coding Networks: Exploiting Temporal Correlations

Efficient Online Learning with Predictive Coding Networks: Exploiting Temporal Correlations | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on Efficient Online Learning with Predictive Coding Networks: Exploiting Temporal Correlations Authors : Darius Masoum Zadeh-Jousdani 0009-0008-6292-6796 [email protected] , Elvin Hajizada , and Eyke Hüllermeier Authors Info & Affiliations https://doi.org/10.22541/au.176463784.45436509/v1 123 views 106 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Robotic systems operating at the edge require efficient online learning algorithms that can continuously adapt to changing environments while processing streaming sensory data. Traditional backpropagation, while effective, conflicts with biological plausibility principles and may be suboptimal for continuous adaptation scenarios. The Predictive Coding (PC) framework offers a biologically plausible alternative with local, Hebbian-like update rules, making it suitable for neuromorphic hardware implementation. However, PC's main limitation is its computational overhead due to multiple inference iterations during training. We present Predictive Coding Network with Temporal Amortization (PCN-TA), which preserves latent states across temporal frames. By leveraging temporal correlations, PCN-TA significantly reduces computational demands while maintaining learning performance. Our experiments on the COIL-20 robotic perception dataset demonstrate that PCN-TA achieves 10% fewer weight updates compared to backpropagation and requires 50% fewer inference steps than baseline PC networks. These efficiency gains directly translate to reduced computational overhead for moving another step toward edge deployment and real-time adaptation support in resource-constrained robotic systems. The biologically-inspired nature of our approach also makes it a promising candidate for future neuromorphic hardware implementations, enabling efficient online learning at the edge. Supplementary Material File (manuscript3.pdf) Download 2.97 MB Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords computational efficiency online learning predictive coding networks temporal amortization temporal correlations Authors Affiliations Darius Masoum Zadeh-Jousdani 0009-0008-6292-6796 [email protected] Institute for Informatics, University of Munich (LMU) View all articles by this author Elvin Hajizada Institute for Informatics, University of Munich (LMU) View all articles by this author Eyke Hüllermeier Institute for Informatics, University of Munich (LMU) View all articles by this author Metrics & Citations Metrics Article Usage 123 views 106 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Darius Masoum Zadeh-Jousdani, Elvin Hajizada, Eyke Hüllermeier. Efficient Online Learning with Predictive Coding Networks: Exploiting Temporal Correlations. Authorea . 02 December 2025. 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