Nature CommunicationsNeurosciencePeer-reviewed journal article
Ex Vivo Cortical Circuits Learn to Predict and Spontaneously Replay Temporal Patterns
Abstract
It has been proposed that prediction and timing are computational primitives of neocortical microcircuits, specifically, that neural mechanisms are in place to allow neocortical circuits to autonomously learn the temporal structure of external stimuli and generate internal predictions. To test this hypothesis, we trained cortical organotypic slices on two specific temporal patterns using dual-optical stimulation. After 24-hours of training, whole-cell recordings revealed network dynamics consistent with training-specific timed prediction. Unexpectedly, there was replay of the learned temporal structure during spontaneous activity. Furthermore, some neurons exhibited timed prediction errors. Mechanistically our results indicate that learning relied in part on asymmetric connectivity between distinct neuronal ensembles with temporally-ordered activation. These findings further suggest that local cortical microcircuits are intrinsically capable of learning temporal information and generating predictions, and that the learning rules underlying temporal learning and spontaneous replay can be intrinsic to local cortical microcircuits and not necessarily dependent on top-down interactions.
- Citations
- 15
- FWCI
- 7.98
- Field percentile
- top 2%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences
Toshitake Asabuki, Tomoki Fukai·eLifePeer-reviewed·Jun 16, 2025
- Predictive learning with local plasticity in excitatory-inhibitory networks
Henrique Aguiar, Matthias H. Hennig·bioRxiv·Sep 4, 2026
- Neural Implementation of Precise Temporal Patterns in Motor Cortex
Yuxiao Ning, Tianyu Zheng, … Shaomin Zhang·bioRxiv·Apr 27, 2022
- Dynamic Predictive Coding: A Model of Hierarchical Sequence Learning and Prediction in the Neocortex
Linxing Preston Jiang, Rajesh P. N. Rao·PLoS Computational BiologyPeer-reviewed·Feb 8, 2024
- Robust associative learning is sufficient to explain structural and dynamical properties of local cortical circuits
Danke Zhang, Chi Zhang, Armen Stepanyants·Journal of NeurosciencePeer-reviewed·Jul 3, 2019
- Learning recurrent dynamics in spiking networks
Christopher M. Kim, Carson C. Chow·eLifePeer-reviewed·Sep 20, 2018