Causally Investigating Cortical Dynamics and Signal Processing by Targeting Natural System Attractors With Precisely...

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Electrical stimulation is key for understanding neural mechanisms of cognition.
• Traditional continuous stimulation creates unnatural brain activity and artifacts.
• Existing methods struggle to precisely control network states for cognitive studies.

Purpose of the Study:

• To develop a real-time, closed-loop electrical stimulation strategy.
• To leverage network attractors for precise state switching and prolonged effects.
• To investigate flexible information routing in the visual cortex using this paradigm.

Main Methods:

• Developed a closed-loop stimulation paradigm based on network phase-response characteristics.
• Utilized a network of interneuronal gamma (ING) oscillators with integrate-and-fire neurons.
• Evaluated information content and signal contamination relative to pulse magnitude and noise.

Main Results:

• Demonstrated the ability to establish desired synchronization states in the neural network.
• Showed that precisely timed perturbations can artificially induce attention-like effects.
• Found that stimulation effects are robust up to a critical noise level.

Conclusions:

• Closed-loop stimulation offers a more natural and precise alternative to continuous methods.
• This approach can be used to selectively route visual signals, mimicking attentional processes.
• The findings have implications for brain-computer interfaces and understanding cognitive functions.