Abstract:
Understanding neural activity through stimulation offers significant potential for neuroscience research and clinical applications. However, experimental setups involving magnetic or electrical stimulation, such as those used to analyze neural responses in the rat sciatic nerve, are often resource-intensive and costly. This project proposes a deep learning-based approach to predict neural response data, eliminating the need for physical stimulation hardware. Leveraging previously collected data from simulations and experimental setups, we aim to train a model that accurately predicts neural excitation under various stimulation conditions. This novel approach has the potential to reduce costs, streamline experiments, and enable scalable analysis of neural activity, fostering advancements in neuroengineering research .
