Predictive coding (PC) is an influential computational model of visual learning and inference in the brain. Classical PC was proposed as a top-down generative model, where the brain actively predicts upcoming visual inputs, and inference minimises the prediction errors. Recent studies have also shown that PC can be formulated as a discriminative model, where sensory inputs predict neural activities in a feedforward manner. However, experimental evidence suggests that the brain employs both generative and discriminative inference, while unidirectional PC models show degraded performance in tasks requiring bidirectional processing. In this work, we propose bidirectional PC (bPC), a PC model that incorporates both generative and discriminative inference while maintaining a biologically plausible circuit implementation. We show that bPC matches or outperforms unidirectional models in their specialised generative or discriminative tasks, by developing an energy landscape that simultaneously suits both tasks. We also demonstrate bPC’s superior performance in two biologically relevant tasks including multimodal learning and inference with missing information, suggesting that bPC resembles biological visual inference more closely. — Read More
Daily Archives: July 14, 2026
Rethinking Predictive Processing
Predictive coding proposes that the brain constructs internal models of the world to continuously predict sensory input and uses resulting errors to refine these models. Over the past several decades, neurophysiological studies have reported activity patterns consistent with this view. However, varied definitions and inconsistent empirical evidence have raised questions about its validity and explanatory scope. In this review, we provide a historical overview of the predictive processing framework and evaluate its empirical support, particularly focusing on sensory prediction error signals. We argue that clarifying what information these signals represent is crucial, as they may appear similar in their responses yet reflect fundamentally different underlying computations. We then revisit predictive coding, highlighting alternative accounts of how sensory prediction error signals encode information. Finally, we outline key directions for future work, aiming to provide a constructive roadmap for the next phase of predictive processing research and to advance our understanding of the neuronal algorithms underlying perception and cognition. — Read More
Video Generation Models are General-Purpose Vision Learners
GenCeption repurposes a pre-trained video generative model into a single unified, general-purpose, feed-forward vision model that solves a wide range of vision tasks with SOTA performance — all steered by text instructions, with exceptional learning efficiency and intriguing emergent behaviors. — Read More
Design-System Maturity: A 6-Dimension Framework
Design-system maturity frameworks often follow a linear progression: you start by building components, move to driving adoption, hit the growing pains of scale, and eventually reach a steady state of governance and evolution.
But design systems don’t mature in a straight line — the reality of design-system work is much messier and more multifaceted than a ladder can capture. This article proposes an alternative that treats design-system maturity as a multidimensional assessment rather than a sequential journey. — Read More
Why ‘Tokenmaxxing’ Is Out And ‘Valuemaxxing’ Is In
…More and more companies are sharing cautionary tales about what happens when tokens are used as a productivity metric. Pushing developers to consume as many tokens as possible can encourage AI adoption, but it also opens the door to catastrophic expenditure.
But is tokenmaxxing over as many suggest?… Now there seems to be a shift in the industry away from high token consumption toward “valuemaxxing.” — Read More
Brain Makes Decisions Far Earlier Than Scientists Thought: Challenge for AI Design
A study published in the Proceedings of the National Academy of Sciences by researchers at the University of Illinois Urbana-Champaign found that decision-making signals appear as early as the primary somatosensory cortex — the brain’s first cortical relay for touch and movement sensation — rather than emerging only in the frontal and premotor regions that classical models assigned as the seat of choice. The finding, which drew fresh coverage Monday from ScienceDaily and the university’s own Grainger College of Engineering, challenges the foundational assumption behind most modern artificial intelligence: that decisions are made at the top of a hierarchy, after information has flowed all the way up from the senses. — Read More
Scientists discovered the brain doesn’t make decisions the way we thought
A new study suggests the brain begins making decisions much earlier than scientists previously thought. Researchers found that even primary sensory regions are influenced by higher brain areas through rapid feedback loops, rather than simply passing information forward. This more dynamic view of brain function could help engineers design future AI systems that think more like biological brains while using far less power. — Read More
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