ArticleAdvances in neural information processing systems2021
Associative Memories via Predictive Coding.
Article in Advances in neural information processing systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 31 citations in OpenAlex.
- Neural Associative Skill Memories for Safer Robotics and Modeling Human Sensorimotor Repertoires.Neural computation · 2025Article
- Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding.PLoS computational biology · 2025Article
- Predictive Coding Model Detects Novelty on Different Levels of Representation Hierarchy.Neural computation · 2025Article
- Learning probability distributions of sensory inputs with Monte Carlo predictive coding.PLoS computational biology · 2024Article
- A sparse quantized hopfield network for online-continual memory.Nature communications · 2024Article
- In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks.Entropy (Basel, Switzerland) · 2024Article
- Fast adaptation to rule switching using neuronal surprise.PLoS computational biology · 2024Article
- Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex.PLoS computational biology · 2024Article
- Predictive coding with spiking neurons and feedforward gist signaling.Frontiers in computational 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
- Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition.Frontiers in computational neuroscience · 2023Article
- The storage capacity of a directed graph and nodewise autonomous, ubiquitous learning.Frontiers in computational neuroscience · 2023Article
- Learning on Arbitrary Graph Topologies via Predictive Coding.Advances in neural information processing systems · 2022Article
- Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models.Proceedings of machine learning research · 2022Article
- Brain-inspired Predictive Coding Improves the Performance of Machine Challenging Tasks.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. In this paper, we present a novel neural model for realizing associative memories, which is based on a hierarchical generative network that receives external stimuli via sensory neurons. It is trained using predictive coding, an error-based learning algorithm inspired by information processing in the cortex. To test the model's capabilities, we perform multiple retrieval experiments from both corrupted and incomplete data points. In an extensive comparison, we show that this new model outperforms in retrieval accuracy and robustness popular associative memory models, such as autoencoders trained via backpropagation, and modern Hopfield networks. In particular, in completing partial data points, our model achieves remarkable results on natural image datasets, such as ImageNet, with a surprisingly high accuracy, even when only a tiny fraction of pixels of the original images is presented. Our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.
Identifiers
35664437PMC7612799W3201359912What 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.