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[2602.15571] Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment
Summary
The paper introduces Direct Kolen-Pollack Predictive Coding (DKP-PC), an innovative approach that enhances the efficiency of predictive coding networks by addressing feedback delays and decay, improving computational performance.
Why It Matters
This research is significant as it proposes a solution to critical limitations in existing predictive coding frameworks, potentially leading to faster and more efficient neural network training. The findings could influence future developments in machine learning and hardware implementations.
Key Takeaways
- DKP-PC reduces error propagation time complexity from O(L) to O(1), enhancing efficiency.
- The method introduces learnable feedback connections, improving error transmission.
- Empirical results show DKP-PC matches or exceeds the performance of standard predictive coding.
- The approach supports hardware-efficient implementations, broadening application potential.
- This research could influence future neural network architectures and training methodologies.
Computer Science > Machine Learning arXiv:2602.15571 (cs) [Submitted on 17 Feb 2026] Title:Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment Authors:Davide Casnici, Martin Lefebvre, Justin Dauwels, Charlotte Frenkel View a PDF of the paper titled Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment, by Davide Casnici and 3 other authors View PDF HTML (experimental) Abstract:Predictive coding (PC) is a biologically inspired algorithm for training neural networks that relies only on local updates, allowing parallel learning across layers. However, practical implementations face two key limitations: error signals must still propagate from the output to early layers through multiple inference-phase steps, and feedback decays exponentially during this process, leading to vanishing updates in early layers. We propose direct Kolen-Pollack predictive coding (DKP-PC), which simultaneously addresses both feedback delay and exponential decay, yielding a more efficient and scalable variant of PC while preserving update locality. Leveraging direct feedback alignment and direct Kolen-Pollack algorithms, DKP-PC introduces learnable feedback connections from the output layer to all hidden layers, establishing a direct pathway for error transmission. This yields an algorithm that reduces the theoretical error propagation time complexity from O(L), with L being the network depth, to O(1), removing depth-dependent delay in error ...
