arXiv:2602.07697v2 Announce Type: replace Abstract: Predictive coding (PC) is a biologically plausible alternative to standard backpropagation (BP) that minimises an energy function with respect to network activities before updating weights. Recent work has improved the training stability of deep PC networks (PCNs) by leveraging some BP-inspired reparameterisations. However, the full scalability and theoretical basis of these methods remain unclear. To address this gap, we study the infinite width and depth limits of PCNs. For linear residual networks, we show that the set of width- and depth-
The continuous push for more efficient and biologically plausible AI training methods drives ongoing research into alternatives like predictive coding, especially as deep learning models grow in complexity.
Improving the theoretical understanding and scalability of predictive coding networks (PCNs) could lead to more robust, efficient, and potentially AGI-relevant training paradigms, offering an alternative to standard backpropagation.
This research provides deeper theoretical insights into the infinite width and depth limits of PCNs, potentially validating their scalability for complex AI tasks and guiding future architectural designs.
- · AI researchers
- · Deep learning framework developers
- · Hardware architects for neuromorphic computing
- · AI models reliant solely on inefficient training methods
Enhanced theoretical foundations for alternative AI training algorithms will accelerate the development of biologically-inspired neural networks.
More stable and scalable predictive coding could enable the creation of AI systems that learn more efficiently and with less data, akin to biological systems.
A breakthrough in biologically plausible learning might eventually contribute to the development of artificial general intelligence (AGI) that operates on fundamentally different principles than current deep learning.
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Read at arXiv cs.LG