Abstract
Design of hardware based on biological principles of neuronal computation and
plasticity in the brain is a leading approach to realizing energy- and
sample-efficient artificial intelligence and learning machines. An important
factor in selection of the hardware building blocks is the identification of
candidate materials with physical properties suitable to emulate the large
dynamic ranges and varied timescales of neuronal signaling. Previous work has
shown that the all-or-none spiking behavior of neurons can be mimicked by
threshold switches utilizing phase transitions. Here we demonstrate that
devices based on a prototypical metal-insulator-transition material, vanadium
dioxide (VO2), can be dynamically controlled to access a continuum of
intermediate resistance states. Furthermore, the timescale of their intrinsic
relaxation can be configured to match a range of biologically-relevant
timescales from milliseconds to seconds. We exploit these device properties to
emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a
neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment,
and ultraslow (~1 s) biochemical signaling involved in temporal credit
assignment for a recently discovered biological mechanism of one-shot learning.
Simulations show that an artificial neural network using properties of VO2
devices to control an agent navigating a spatial environment can learn an
efficient path to a reward in up to 4 fold fewer trials than standard methods.
The phase relaxations described in our study may be engineered in a variety of
materials, and can be controlled by thermal, electrical, or optical stimuli,
suggesting further opportunities to emulate biological learning.