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Related Experiment Video

Updated: Jun 25, 2026

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Contribution of the Na⁺/K⁺ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Published on: May 9, 2021

The effect of learning on bursting.

Jan Stegenga¹, Joost Le Feber, Enrico Marani

¹Institute of Biomedical Technology (BMTI), Department of Electrical Engineering, Mathematics and Computer Science, Biomedical Signals and Systems Group (BSS), University of Twente, Enschede 7500 AE, The Netherlands. j.stegenga@utwente.nl

IEEE Transactions on Bio-Medical Engineering

|March 11, 2009

Summary

Learning a new stimulus-response (SR) relationship using conditional repetitive stimulation (CRS) alters neuronal network activity. The whole network participates in learning, but changes are more pronounced along the specific SR pathway.

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Area of Science:

Neuroscience
Computational Neuroscience
Cellular Neuroscience

Background:

Neuronal networks in culture can learn new stimulus-response (SR) relationships.
Conditional repetitive stimulation (CRS) is an effective training algorithm for strengthening weak SR connections.
The role of the broader neuronal network during CRS-mediated learning remains unclear.

Purpose of the Study:

To investigate the involvement of the entire neuronal network during the learning of a new SR relationship via CRS.
To analyze the impact of CRS on spontaneously occurring network bursts.
To differentiate between global network changes and local, path-specific learning.

Main Methods:

Cultured neuronal networks on multielectrode arrays were trained using CRS.
Repetitive focal electrical stimulation was applied at a low rate (<1 Hz) until a target SR success ratio was met.
Network burst activity and firing rate profiles were analyzed to assess changes in network dynamics.

Main Results:

Burst profiles, representing summed network activity, showed accelerated shape changes during CRS, indicating global network involvement.
Analysis of single-electrode-activity phase profiles revealed a local, path-specific component to learning.
Phase profiles associated with the trained SR pathway changed significantly more than those not involved in the SR relationship.

Conclusions:

The entire neuronal network participates in adapting to and incorporating a newly learned SR relationship through CRS.
Learning involves both global network reorganization and specific modifications along the trained pathway.
The precise mechanisms driving the observed changes in phase profiles require further investigation.