Bioinspired navigation based on distributed mechanoreception in the leech

Abstract Water waves help aquatic animals locate environmental stimuli. While previous studies suggest that leech mechanoreceptors have evolved to respond to relevant wave frequencies, the underlying neural processing remains unclear. This work presents a simplified computational model that mimics leech goal seeking behavior in an agent-based simulation. A simulated leech was tasked with finding the source of an artificial water wave stimulus. The agent's distributed mechanoreceptor array detected wave motion, which was processed with a computational neuroscience Winner-Take-All (WTA) framework to generate motion commands. The computational model's performance aligned with data from animal experiments. The model also suggested how different patterns of sensor ablation or placement might affect navigation performance. Our model can complement animal experiments by enabling questions to be posed that are challenging to address with live animals. Our study may also provide insights into novel approaches for processing data from multiple sensors in man-made systems.