English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Design of hardware based on biological principles of neuronal computation andplasticity in the brain is a leading approach to realizing energy- andsample-efficient artificial intelligence and learning machines. An importantfactor in selection of the hardware building blocks is the identification ofcandidate materials with physical properties suitable to emulate the largedynamic ranges and varied timescales of neuronal signaling. Previous work hasshown that the all-or-none spiking behavior of neurons can be mimicked bythreshold switches utilizing phase transitions. Here we demonstrate thatdevices based on a prototypical metal-insulator-transition material, vanadiumdioxide (VO2), can be dynamically controlled to access a continuum ofintermediate resistance states. Furthermore, the timescale of their intrinsicrelaxation can be configured to match a range of biologically-relevanttimescales from milliseconds to seconds. We exploit these device properties toemulate three aspects of neuronal analog computation: fast (~1 ms) spiking in aneuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment,and ultraslow (~1 s) biochemical signaling involved in temporal creditassignment for a recently discovered biological mechanism of one-shot learning.Simulations show that an artificial neural network using properties of VO2devices to control an agent navigating a spatial environment can learn anefficient 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 ofmaterials, and can be controlled by thermal, electrical, or optical stimuli,suggesting further opportunities to emulate biological learning.

Temporal credit assignment for one-shot learning utilizing a phase transition material