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. 2024 Feb 13;21:23. doi: 10.1186/s12984-024-01318-9

Table 2.

Neuroplasticity: state-of-the-art and future directions

State-of-the-art a. Hebbian learning models: spike-time dependence, long-term potentiation,
homeostatic plasticity, and long-term depression
b. Models at distinct spatial scales: microscale, mesoscale, phenotypic / behaviour.
c. Brain-machine interface algorithms that relate neuronal activity to behaviour.
Future directions a. Experiments and models that link the multiple spatial and temporal scales of
neuroplasticity that can be used to developed informed and optimized treatment.
b. Improving brain machine interface (BMI) design and developing brain machine
interface algorithms that enhance neuroplasticity.
c. More fundamental studies that examine understand the influence of movement
on structural and functional neuroplasticity.