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The paper proposes a five-layer implementation architecture for the S3Q theory of machine consciousness, composing published computational primitives into a pipeline to achieve simultaneous situatedness, simulation, and structural coherence conditions for qualia.
PC-ALM is a local training method that uses layer-local dynamical systems to propagate supervision credit, enabling the training of up to 1000-layer networks without backpropagation while nearly matching its performance.
The Deepity C++ library implements optimized Predictive Coding Networks to achieve near-backpropagation performance on MNIST, with 97.73% accuracy in 59.5 seconds.
本文证明现代深度网络中预测编码（PC）使用的Jacobian转置乘积可以分解为局部可用的项，从而消除了自动求导反向传播的需要。提出的WF-Act-PC方法在CIFAR-10/100和Tiny-ImageNet上匹配甚至超越经过调优的反向传播基线，且性能随深度增加而提升。
This paper shows that predictive coding networks compute the same gradients as backpropagation in the limit of width much larger than depth, bridging biological learning and standard neural network training.
This paper investigates the developmental conditions under which a minimal predictive neural system (a 192-dimensional GRU) can distinguish self-caused changes from world-caused changes, identifying four necessary conditions for agency and introducing a metric called agency gain.
This paper tracks how different learning rules (backprop, feedback alignment, predictive coding, STDP) affect the alignment of CNN representations with human fMRI across training. It finds that backprop destroys V1 alignment in one epoch, while local rules preserve it, suggesting a trade-off between building higher-level representations and retaining early visual features.
This paper tracks how supervised training with different learning rules (backpropagation, feedback alignment, predictive coding, STDP) degrades alignment between neural network representations and early visual cortex fMRI data, finding that untrained networks often match or exceed trained ones in V1 alignment.
AdaCodec reduces video encoding redundancy in multimodal LLMs by transmitting full visual tokens only when scene prediction fails, otherwise using compact inter-frame change descriptions. It outperforms per-frame RGB baselines at matched token budgets and achieves better or comparable results with significantly fewer tokens, reducing time-to-first-token from 9.26s to 1.62s.
The paper introduces closed-form predictive coding via hierarchical Gaussian filters that restore precision-weighted prediction errors, yielding faster and more efficient training without global error signals, outperforming backpropagation on certain tasks.
Swift Sampling is a training-free algorithm that uses Taylor expansion to identify high-information moments in long-form videos by detecting deviations from predicted feature trajectories, improving accuracy on video QA tasks with minimal computational overhead.
Explores how close a biologically plausible Hebbian agent can get to PPO on Pong, finding only a 2% gap but identifying catastrophic forgetting under self-play as a key bottleneck.
RoboMemArena introduces a large-scale benchmark for evaluating robotic memory across 26 complex tasks with real-world validation, alongside PrediMem, a dual-system vision-language-action model that improves memory management through predictive coding.