ArticleProceedings of the National Academy of Sciences of the United States of America2025
Learning predictive signals within a local recurrent circuit.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Correctness is its own reward: bootstrapping error signals in self-guided reinforcement learning.bioRxiv : the preprint server for biology · 2025Article
- Learning predictive signals within a local recurrent circuit.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
The predictive coding hypothesis proposes that top-down predictions are compared with incoming bottom-up sensory information, with prediction errors signaling the discrepancies between these inputs. While this hypothesis explains the presence of prediction errors, recent experimental studies suggest that prediction error signals can emerge within a local circuit, that is, from bottom-up sensory input alone. In this paper, we test whether local circuits alone can generate predictive signals by training a recurrent spiking network using local plasticity rules. Our network model replicates experimentally observed features of prediction errors, such as biphasic neural activity patterns and context dependency. Our findings shed light on how synaptic plasticity can shape prediction errors and enable the acquisition and updating of an internal model of sensory input within a recurrent neural network.
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Registered trials
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