ArticleAdvances in neural information processing systems2022
Learning on Arbitrary Graph Topologies via Predictive Coding.
Article in Advances in neural information processing systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- pyhgf: A neural network library for predictive coding.PLoS computational biology · 2026Article
- The phantastic organ: predictive processing in the brain.Frontiers in psychology · 2026Review
- Predictive Coding Model Detects Novelty on Different Levels of Representation Hierarchy.Neural computation · 2025Article
- Brain-like variational inference.ArXiv · 2025Article
- Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.Nature neuroscience · 2024Article
- Predictive coding with spiking neurons and feedforward gist signaling.Frontiers in computational neuroscience · 2024Article
- Imagination vs. routines: festive time, weekly time, and the predictive brain.Frontiers in human neuroscience · 2024Article
- Recurrent predictive coding models for associative memory employing covariance learning.PLoS computational biology · 2023Article
- Sequential Memory with Temporal Predictive Coding.Advances in neural information processing systems · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network. This process is highly effective when the goal is to minimize a specific objective function. However, it does not allow training on networks with cyclic or backward connections. This is an obstacle to reaching brain-like capabilities, as the highly complex heterarchical structure of the neural connections in the neocortex are potentially fundamental for its effectiveness. In this paper, we show how predictive coding (PC), a theory of information processing in the cortex, can be used to perform inference and learning on arbitrary graph topologies. We experimentally show how this formulation, called
Identifiers
37090087PMC7614467W4366819521What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.