| Autor: |
Galloni, Alessandro R., Yifan Yuan, Minning Zhu, Haoming Yu, Bisht, Ravindra S., Chung-Tse Michael Wu, Grienberger, Christine, Ramanathan, Shriram, Milstein, Aaron D. |
| Předmět: |
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| Zdroj: |
Proceedings of the National Academy of Sciences of the United States of America; 4/23/2024, Vol. 121 Issue 17, p1-12, 35p |
| Abstrakt: |
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 AI 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 material 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 fourfold 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 in neuromorphic hardware. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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