scholarly journals A Compact Model for Stochastic Spike-Timing-Dependent Plasticity (STDP) Based on Resistive Switching Memory (RRAM) Synapses

2020 ◽  
Vol 67 (7) ◽  
pp. 2800-2806
Author(s):  
Stefano Bianchi ◽  
Giacomo Pedretti ◽  
Irene Munoz-Martin ◽  
Alessandro Calderoni ◽  
Nirmal Ramaswamy ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Spike Timing ◽  
Compact Model ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Resistive Switching Memory ◽  
Switching Memory

A synaptic device built in one diode–one resistor (1D–1R) architecture with intrinsic SiOx-based resistive switching memory

2016 ◽  
Vol 1 (4) ◽  
Author(s):  
Yao-Feng Chang ◽  
Burt Fowler ◽  
Ying-Chen Chen ◽  
Fei Zhou ◽  
Chih-Hung Pan ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Silicon Oxide ◽  
Complementary Metal Oxide Semiconductor ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Oxide Semiconductor ◽  
Resistive Switching Memory ◽  
Term Potentiation ◽  
Switching Memory

Abstract We realize a device with biological synaptic behaviors by integrating silicon oxide (SiOx) resistive switching memory with Si diodes to further minimize total synaptic power consumption due to sneak-path currents and demonstrate the capability for spike-induced synaptic behaviors, representing critical milestones for the use of SiO2-based materials in future neuromorphic computing applications. Biological synaptic behaviors such as long-term potentiation, long-term depression, and spike-timing dependent plasticity are demonstrated systemically with comprehensive investigation of spike waveform analyses and represent a potential application for SiOx-based resistive switching materials. The resistive switching SET transition is modeled as hydrogen (proton) release from the (SiH)2 defect to generate the hydrogenbridge defect, and the RESET transition is modeled as an electrochemical reaction (proton capture) that re-forms (SiH)2. The experimental results suggest a simple, robust approach to realize programmable neuromorphic chips compatible with largescale complementary metal-oxide semiconductor manufacturing technology.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

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