Mimicking neural habituation behavior with pristine leaf-based memristors

Neuromorphic computing systems allow electronics to mimic the behavior of biological neurons and hold potential for high-efficiency computation needed for artificial intelligence. One class of electronic units capable of mimicking neural behavior is resistive memory devices, also known as memristors. While the most commonly studied memristors are solid-state in nature, there is a growing interest in the development of ion-rich fluidic memristors since they have a higher level of versatility to mimic biological neurons and synapses, which are themselves fluidic systems with mobile ions as neuroreceptors. Here, we present our work on elucidating the ion transport mechanism in leaf-based capacitive-coupled memristive devices and their use in mimicking habituation, a neural behavior where an organism’s response to repeated applied stimulus diminishes. This work demonstrates the potential that leaf structures hold in serving as biomimetic architecture for the development of highly effective fluidic memristors for simulating neural behavior.