Stimulus Optimization Using the Artificial Bee Colony Algorithm for Visual Neuroprostheses

Optimization of electrical stimulation patterns can enhance the performance of visual neuroprosthetics by replicating the selective and temporally precise neural responses characteristic of natural vision.

An in silico approach employing the Artificial Bee Colony (ABC) algorithm was developed to optimize electrical stimulus waveforms that evoke physiologically realistic responses in retinal ganglion cells (RGCs). The optimization was guided by a computational model of ON and OFF RGCs, with the goal of matching recorded neural responses from the rat dorsolateral geniculate nucleus (dLGN). The ABC algorithm iteratively modified the amplitude envelope of charge-balanced, biphasic pulse trains to maximize similarity between simulated and recorded responses.

The ABC algorithm consistently achieved $\geq$80% cross-correlation with target neural responses within 2000 iterations. It also identified single optimized waveforms capable of eliciting distinct responses in neighboring neurons, indicating potential for simultaneous physiological responses with electrical stimulations. Under constrained computational conditions, ABC demonstrated superior accuracy compared to alternative optimization algorithms.

The proposed ABC-based optimization approach effectively generates neural activation patterns closely resembling natural responses, supporting its use for real-time and adaptive stimulation control in visual neuroprosthetic systems.

This study introduces a biologically inspired optimization framework that advances the development of intelligent stimulation strategies to enhance the functionality of next-generation visual neuroprostheses.