Does the D.C. Response of Memristors Allow Robotic Short-Term Memory and a Possible Route to Artificial Time Perception?
This work addresses the problem of improving task switching and learning in robotics and AI through novel hardware-based memory models, though it appears incremental as it builds on existing memristor research without presenting concrete experimental results.
The paper explores using memristor networks to model short-term memory and oscillatory dynamics, similar to neural processes, and suggests this could enhance robot task switching and learning, potentially leading to artificial time perception.
Time perception is essential for task switching, and in the mammalian brain appears alongside other processes. Memristors are electronic components used as synapses and as models for neurons. The d.c. response of memristors can be considered as a type of short-term memory. Interactions of the memristor d.c. response within networks of memristors leads to the emergence of oscillatory dynamics and intermittent spike trains, which are similar to neural dynamics. Based on this data, the structure of a memristor network control for a robot as it undergoes task switching is discussed and it is suggested that these emergent network dynamics could improve the performance of role switching and learning in an artificial intelligence and perhaps create artificial time perception.