arXiv:2606.08374v1 Announce Type: cross Abstract: We recast predictive coding as continuous-time proximal gradient descent applied to a regularized maximum-a-posteriori (MAP) objective. We study first a single-level problem and then a multi-level hierarchy. For the single-level problem, we show that proximal gradient descent is precisely a leaky firing-rate network: the membrane leak, the effective recurrent matrix, the local synaptic drive, and the static nonlinearity all follow from one optimization principle, and the resulting circuit is the one proposed by Rao and Ballard. The prior select
The paper leverages recent advancements in proximal gradient methods and neural network architectures to provide a fresh perspective on predictive coding, a fundamental theory in neuroscience and AI.
This research provides a more robust mathematical framework for understanding and building intelligent systems, potentially leading to more efficient and biologically plausible AI models.
The explicit connection between predictive coding and continuous-time proximal gradient descent offers a new computational lens for designing and analyzing AI architectures, potentially converging neuroscience and AI research more closely.
- · AI researchers
- · Machine learning framework developers
- · Neuroscience research institutions
Improved theoretical understanding of brain-like AI architectures.
Development of novel AI models that are more energy-efficient and capable of learning with less data.
Accelerated progress towards general AI systems that mimic biological intelligence more effectively.
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