Stimulus Optimization

Stimulus optimization focuses on designing and delivering optimal stimuli to elicit desired responses in biological systems, particularly within the context of neural interfaces and sensory prostheses. Current research employs various approaches, including deep learning models (like convolutional neural networks and autoencoders) and optimization algorithms (such as Bayesian optimization and evolutionary algorithms), often combined to address the high dimensionality and individual variability inherent in biological systems. These advancements aim to improve the efficacy and naturalness of sensory restoration in neuroprosthetics and enhance our understanding of neural coding by identifying the most discriminative stimuli for different neuron types. The ultimate goal is to create more effective and personalized therapies for sensory impairments and to gain deeper insights into neural processing.