Open AccessIn Materia Neuron Spiking Plasticity for Sequential Event Processing Based on Dual‐Mode Memristor
Author(s) -
Shan Linbo,
Wang Zongwei,
Bao Lin,
Bao Shengyu,
Qin Yabo,
Ling Yaotian,
Bai Guandong,
Robertson John,
Cai Yimao,
Huang Ru
Publication year - 2022
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202100264
Subject(s) - neuromorphic engineering , spiking neural network , computer science , memristor , bursting , neuroscience , adaptation (eye) , neuron , artificial neural network , spike (software development) , spike timing dependent plasticity , biological neuron model , artificial intelligence , synaptic plasticity , chemistry , physics , biology , biochemistry , receptor , software engineering , quantum mechanics
Artificial neurons are the fundamental elements in neuromorphic computing systems. Studies have revealed neuronal spike‐rate adaptation owing to intrinsic plasticity that neurons will adapt to the spiking patterns and store the events in the background spiking through clustered neuronal spiking. The event can be reactivated by specific retrieval clues instead of solely relying on synaptic plasticity. However, the neural adaptation, as well as the interactive adaptations of neuronal activity for information processing, have not been implemented. Herein, an artificial adaptive neuron via in materia modulation of the VO 2 /HfO 2 based dual‐mode memristor is demonstrated. By changing the conductance of the HfO 2 layer, the firing threshold can be modulated, thus the excitability and inhibition can be adjusted according to the previous stimuli without any complex peripherals, showing an adaptive firing rate even under the same stimuli. The artificial neuron clusters can emulate the concept of neuronal memory and neural adaptation, demonstrating spatiotemporal encoding capabilities via the correlated neural firing patterns. This conceptual work provides an alternative way to expand the computation power of spiking neural networks by exploiting the neural adaptation and could be enlightenment to maximize the synergy across both synapse and neuron in neuromorphic computing systems.