ArticlePLoS computational biology2024
Fast adaptation to rule switching using neuronal surprise.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Remembering the "when": Hebbian memory models for the time of past events.PLoS computational biology · 2026Article
- Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks.Nature communications · 2026Article
- Global error signal guides local optimization in mismatch calculation.Nature communications · 2026Article
- Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective.Patterns (New York, N.Y.) · 2025Review
- Exploring the Architectural Biases of the Cortical Microcircuit.Neural computation · 2025Article
- Article
- Article
- Neurons of Macaque Frontal Eye Field Signal Reward-Related Surprise.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural activity after an imbalance of excitation and inhibition. The surprise signal modulates synaptic plasticity via a three-factor learning rule which increases plasticity at moments of surprise. The surprise signal remains small when transitions between sensory events follow a previously learned rule but increases immediately after rule switching. In a spiking network with several modules, previously learned rules are protected against overwriting, as long as the number of modules is larger than the total number of rules-making a step towards solving the stability-plasticity dilemma in neuroscience. Our model relates the subjective notion of surprise to specific predictions on the circuit level.
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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.