Predictive coding equals proximal gradient descent on MAP problems, with priors setting nonlinearities via proximal operators and yielding leaky firing-rate networks plus hierarchical MRFs.
Canonical microcircuits for predictive coding
4 Pith papers cite this work. Polarity classification is still indexing.
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Backpropagated gradients from vision models predict higher visual cortex signals but diverge from brain hierarchies in spatial and temporal organization.
Optimal LPC networks achieve near-minimal response times without trade-offs in energetic cost or robustness, and modular structures with reduced lateral connections match all-to-all networks in performance.
LLM agents use a Cartesian split between learned prediction and engineered control, enabling modularity but creating sensitivity and bottlenecks unlike integrated biological systems.
citing papers explorer
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Predictive Coding with Bayesian Priors via Proximal Gradients
Predictive coding equals proximal gradient descent on MAP problems, with priors setting nonlinearities via proximal operators and yielding leaky firing-rate networks plus hierarchical MRFs.
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Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
Backpropagated gradients from vision models predict higher visual cortex signals but diverge from brain hierarchies in spatial and temporal organization.
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Response time of lateral predictive coding and benefits of modular structures
Optimal LPC networks achieve near-minimal response times without trade-offs in energetic cost or robustness, and modular structures with reduced lateral connections match all-to-all networks in performance.
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The Cartesian Cut in Agentic AI
LLM agents use a Cartesian split between learned prediction and engineered control, enabling modularity but creating sensitivity and bottlenecks unlike integrated biological systems.