Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Neural Engineering

Background:

• Spike-triggered stimulation via Bidirectional Brain-Computer-Interfaces (BBCI) artificially strengthens neural connections in the motor cortex (MC).
• Effective spike-stimulus delays align with spike-timing-dependent plasticity (STDP) rules, indicating STDP's role in synaptic modification.
• Mechanisms of STDP modification by neural implants and its circuit-level impact remain unclear.

Purpose of the Study:

• To develop a computational model capturing neural and synaptic dynamics during BBCI conditioning.
• To gain mechanistic insights into spike-triggered plasticity.
• To identify optimal operating regimes for BBCIs and predict conditioning efficacy.

Main Methods:

• Developed a recurrent neural network model with probabilistic spiking and plastic synapses.
• Simulated BBCI conditioning protocols.
• Employed analytical calculations and numerical simulations.

Main Results:

• The model successfully replicated established and novel experimental findings in BBCI conditioning.
• Mechanistic insights into spike-triggered conditioning were provided.
• Optimal operational regimes for BBCIs were derived, with predictions for conditioning efficacy under different cortical activity patterns.

Conclusions:

• The developed model provides a powerful tool for understanding BBCI-induced neural plasticity.
• STDP is confirmed as a key driver of plasticity in BBCI protocols.
• The study offers guidance for optimizing BBCI design and application for therapeutic interventions.