23 papers · ranked by Valyu relevance
Alexander D. Shaw, Rachael L. Sumner, Lioba C. S. Berndt
Predictive coding offers a powerful computational framework for understanding brain function and psychiatric disorders at a mechanistic level. This perspective synthesizes advances in computational psychiatry, proposing that mental disorders can be conceptualized as specific alterations in the brain’s predictive…
Albandri Sultan Alotaibi
Predictive coding (PC) has become a central framework in contemporary cognitive neuroscience, proposing that the brain operates as a hierarchical inference system that continuously minimizes the mismatch between predicted and actual sensory input. Its extension into clinical neuroscience has been accompanied by…
Davide Noè, Hideaki Yamamoto, Yuichi Katori, Shigeo Sato
The predictive coding framework offers a compelling model for temporal signal processing in the cortex. Recent studies explored its implementation in spiking architectures using Hebbian plasticity rules or offline learning; however, a biologically inspired model that enables gradient-based minimization of prediction…
Victor Navarro, Stefan Brugger, Noham Wolpe, Jessica Harding + 2 more
Predictive coding has influenced many conceptual accounts of delusions, the bizarre and distressing beliefs that accompany a range of neuropsychiatric conditions. However, these explanations remain incomplete and have rarely been tested directly using formal modelling. Here, we present a formal account of delusional…
Antony W. N'dri, Thomas Barbier, Céline Teulière, Jochen Triesch
The ability to predict the future is of great value for biological and artificial cognitive systems alike. However, successfully predicting the future typically requires maintaining a memory of the recent past. It is currently unclear how biological or artificial spiking neural networks can learn to maintain past…
Romain Brette
Anticipation is a hallmark of all living phenomena, from bacteria to humans. If I notice a cloudy sky, I may take an umbrella to avoid getting wet. In cyanobacteria, which are photosynthetic bacteria, the chromosome decompacts shortly before dawn thanks to circadian rhythms, so that gene expression is favored when it…
Ashena Gorgan Mohammadi, Manu Srinath Halvagal, Friedemann Zenke
Tracking prey or recognizing a lurking predator is as crucial for survival as anticipating their actions. To guide behavior, the brain must extract information about object identities and their dynamics from entangled sensory inputs. How it accomplishes this feat remains an open question. Predictive coding theories…
Gaspard Oliviers, Elene Lominadze, Rafal Bogacz
Predictive Coding (PC) is an influential account of cortical learning. Much of recent work has focused on comparing PC to Backpropagation (BP) to find whether PC offers any advantages. Small scale experiments show that PC enables learning that is more sample efficient and effective in many contexts, though a thorough…
Julian Ng-Kee-Kwong, Mufeng Tang, Thomas Akam, Rafal Bogacz
The ability to extract and exploit temporal structure across diverse tasks is central to human cognition. Neuroscientists have typically relied on recurrent neural networks (RNNs) trained with backpropagation through time (BPTT) when modelling neural and behavioural processes such as decision-making and motor control.…
Henrique Reis Aguiar, Matthias H. Hennig
Predictive coding is a powerful normative framework for understanding cortical computation, but it is still an open question how biologically plausible networks with local plasticity support predictive inference and representation learning. In this work we show that a recurrent excitatory-inhibitory circuit with purely…
A. B. Lao-Rodríguez, S. Cacciato-Salcedo, M. S. Malmierca
Predictive coding theory proposes that the brain continuously minimizes prediction errors through hierarchical inference. Increasing evidence, however, suggests that its explanatory power depends not only on prediction generation, but also on precision weighting, the process through which the brain estimates the…
Shogo Ohmae, Keiko Ohmae
Title: Summary Recent advances in general-purpose AI provide new insights into how the neocortex and cerebellum, despite their uniform circuit architectures, support diverse functions and human intelligence. Beyond the traditional focus on visual processing, this paper offers a cross-domain comparison of the brain and…
Hamed Nejat, Alexander Maier, Jesse Spencer-Smith, André M. Bastos
Fragmentation is common in interdisciplinary fields with diverse methods and theoretical commitments. Predictive coding neuroscience is a clear example: its literature spans computational theory, electrophysiology, imaging, behavior, and modeling, creating a synthesis problem that conventional meta-analysis cannot…
Yamada, Yohei, Zenas C. Chao
The brain predicts the external world through an internal model refined by prediction errors. A complete prediction specifies what will happen, when it will happen, and with what probability, a construct we call the "prediction object." Existing models usually capture only what and when, omit probabilities, and rely on…
Andrew L. Smith, Linxing Preston Jiang, Jason K. Eshraghian, Matthew S. Bull + 1 more
Hierarchical predictive coding proposes a compelling hypothesis of brain computation, suggesting that the cortex builds layered predictions to minimize surprise. Yet most models rely on error-coding neurons or generative modeling of unclear biological plausibility. Here, we examine a biologically plausible framework in…
Wu Yonggang
| 1. Introduction4 | | |----------------------------------------------------------------------|--| | 2. Module 1: Visual processing system (VPS) module4 | | | 2.1 Complementary plasticity hypothesis (CPH)5 | | | 2.2 Training and tuning6 | | | 2.3 Specificity and invariance7 | | | 2.4 Invariance through excitatory…
Sean Niklas Semmler
Understanding intelligence and consciousness requires moving beyond cataloging cognitive abilities to identifying the fundamental operations that produce them. This paper argues that intelligence emerges from a single purpose:forming, refining, and integrating causal connections between signals, actions, internal…
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
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Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
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Theoretical prediction of enantioselectivity for broad range of substrates in a given reaction has long been a formidable challenge, traditionally replaced by labor-intensive screening of multiple conditions. Until recently this remained an unaddressed problem in asymmetric catalysis under data-limited scenarios, yet…
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Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
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We present an updated version of a priori computational intelligence, a methodology that integrates semi-empirical Quantum Mechanics calculations with supervised machine learning to predict optimal reaction conditions without prior extensive experimental work. First, the synergy between semi-empirical calculations and…
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Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…