Controlling the probability of a series of seemingly random events is the key to mimicking the human brain to optimize neuromorphic learning.
A new, flexible, and self-powered sensor made by magnetoelectric materials can convert mechanical stimuli to electrical signals for robots with a “soft touch”.
Neuromorphic devices have great potential in the development of multifunctional intelligent artificial perception learning systems.
Soft robotic devices powered by glucose are paving the way for artificial muscles.
Designing a low-cost human-machine interface with specific function and desirable performance characteristics.
There are new top papers in Advanced Intelligent Systems special series. All included papers are free to read for a limited time!