An Integrate-and-Fire Spiking Neural Network Model Simulating Artificially Induced Cortical Plasticity

Area of Science:

• Computational Neuroscience
• Neural Plasticity
• Brain-Computer Interfaces

Background:

• Cortical plasticity is crucial for learning and adaptation.
• Previous studies used non-human primates (NHPs) and bidirectional brain-computer interfaces (BCIs) to investigate plasticity.
• Conditioning protocols involved closed-loop and open-loop stimulation based on neural or physiological signals.

Purpose of the Study:

• To develop and validate an integrate-and-fire (IF) spiking neural network model.
• To incorporate spike-timing-dependent plasticity (STDP) within the model.
• To simulate experimental outcomes of cortical plasticity induced by various conditioning protocols.

Main Methods:

• An integrate-and-fire (IF) spiking neural network with 360 units was developed.
• The model incorporated spike-timing-dependent plasticity (STDP) for synaptic modification.
• Simulations replicated four distinct conditioning protocols, including closed-loop BCI and open-loop stimulation.

Main Results:

• The IF network with STDP successfully simulated experimental results from four conditioning protocols.
• The model captured experimentally observed cortical plasticity effects.
• The model provided insights into underlying network dynamics and predicted outcomes for novel protocols.

Conclusions:

• A simple voltage-based IF model with STDP can effectively capture mechanisms of targeted plasticity.
• Closed-loop stimulation strategies are effective in inducing specific cortical plasticity.
• The model serves as a valuable tool for understanding and predicting neural plasticity.