Neural SDE-based spike control of noisy neurons

Area of Science:

• Computational Neuroscience
• Neuro-engineering
• Biophysics

Background:

• Controlling individual neuron spike timing is crucial for treating neurological disorders.
• Real-world neural control applications require methods robust to biological noise and diverse neuron firing patterns.
• Existing models often simplify neural dynamics or ignore environmental noise.

Purpose of the Study:

• To develop a novel method for precise spike timing control in neurons using Neural Stochastic Differential Equations (Neural SDE).
• To ensure the method's effectiveness across various neuron types and under different noise conditions.
• To enable neuron-specific control signals adaptable to individual firing characteristics.

Main Methods:

• Utilized the Izhikevich model to capture diverse neuronal firing behaviors.
• Employed Neural Stochastic Differential Equations (Neural SDE) for spike timing control.
• Implemented iterative training of external currents via stochastic gradient descent and back-propagation to minimize firing and timing errors.

Main Results:

• Achieved precise spike timing control across regular spiking, bursting, and fast spiking neuron models.
• Demonstrated robustness and effectiveness even under significant noise perturbations.
• Observed particularly high precision in controlling early spike events.

Conclusions:

• The proposed Neural SDE framework offers a robust and generalizable approach to neural spike timing control.
• The method directly accounts for biological noise and complex intrinsic dynamics, outperforming conventional methods.
• Results suggest suitability for real-world applications like neuroprosthetics and adaptive closed-loop therapeutic systems.