Speaker
Description
Transcranial direct current stimulation (tDCS) is increasingly used to modulate motor learning and rehabilitation, yet its effects vary depending on polarity, intensity, electrode montage, and timing. Traditional models based on Hebbian or homeostatic plasticity only partially explain these inconsistencies. We propose that homeostatic structural plasticity (HSP) can offer a more comprehensive explanation. Using a spiking neural network model governed by HSP, we simulated motor learning alongside different tDCS protocols.
Our results show that the timing and spatial targeting of tDCS critically determine its impact on motor learning. tDCS applied before learning had minimal effects, slightly reducing connectivity when applied uniformly. During learning, targeted anodal stimulation enhanced engram connectivity, while cathodal or uniform stimulation weakened it. When applied after learning, only targeted cathodal tDCS increased engram strength. Non-specific or strong stimulation tended to distort engram structure.
These findings suggest that both facilitatory and inhibitory effects of tDCS observed in human studies can be explained through changes in engram connectivity under HSP. Our model provides a mechanistic framework for understanding how tDCS modulates memory formation depending on application parameters, potentially guiding more effective and individualized stimulation protocols.
Acknowledgements
Additional support by the German Research Foundation (DFG) through EXC 1086, and by the state of Baden-W¨urttemberg through bwHPC and the German Research Foundation (DFG) through INST 39/963-1 FUGG is acknowledged. The authors thank Julia V. Gallinaro for establishing the fundamental HSP model. We also thank Uwe Grauer from the Bernstein Center Freiburg, as well as Bernd Wiebelt and Michael Janczyk from the Freiburg University Computing Center for their assistance with HPC applications.
| Preferred form of presentation | Poster & advertising flash talk |
|---|---|
| Topic area | Models and applications |
| Keywords | tDCS, motor learning, homeostatic structural plasticity, spiking neural network, cell assembly |
| Speaker time zone | UTC+2 |
| I agree to the copyright and license terms | Yes |
| I agree to the declaration of honor | Yes |
