Effect of heavy-ion on frequency selectivity of semiconducting polymer/electrolyte heterojunction

RSC Advances ◽  
2015 ◽  
Vol 5 (119) ◽  
pp. 98110-98117 ◽  
Author(s):  
W. S. Dong ◽  
F. Zeng ◽  
S. H. Lu ◽  
X. J. Li ◽  
C. T. Chang ◽  
...  
Keyword(s):  
Polymer Electrolyte ◽  
Pulse Shape ◽  
Heavy Ion ◽  
Spike Rate ◽  
Frequency Selectivity ◽  
Semiconducting Polymer ◽  
Dependent Plasticity ◽  
Rate Dependent

Long-term bidirectional frequency selectivity has been achieved in MEH-PPV/PEO–Nd3+cells, which suggests spike-rate-dependent plasticity learning protocol. It depends on pulse shape due to variation of ionic type.

Sliding threshold of spike-rate dependent plasticity of a semiconducting polymer/electrolyte cell

2016 ◽  
Vol 54 (23) ◽  
pp. 2412-2417 ◽  
Author(s):  
Wenshuai Dong ◽  
Fei Zeng ◽  
Yuandong Hu ◽  
Chiating Chang ◽  
Xiaojun Li ◽  
...  
Keyword(s):  
Polymer Electrolyte ◽  
Spike Rate ◽  
Semiconducting Polymer ◽  
Dependent Plasticity ◽  
Rate Dependent

Frequency-dependent learning achieved using semiconducting polymer/electrolyte composite cells

Nanoscale ◽  
2015 ◽  
Vol 7 (40) ◽  
pp. 16880-16889 ◽  
Author(s):  
W. S. Dong ◽  
F. Zeng ◽  
S. H. Lu ◽  
A. Liu ◽  
X. J. Li ◽  
...  
Keyword(s):  
Polymer Electrolyte ◽  
Spike Rate ◽  
Frequency Dependent ◽  
Semiconducting Polymer ◽  
Dependent Plasticity ◽  
Rate Dependent ◽  
Dependent Learning

Frequency-dependent learning has been achieved using semiconducting polymer/electrolyte composite cells, which realized conventional spike-rate-dependent plasticity learning protocol.

scholarly journals Efficient Resistive Switching and Spike Rate Dependent Plasticity in a New CuCrO2 Memristor for Plausible Neuromorphic Systems

2020 ◽  
Vol 67 (8) ◽  
pp. 3451-3458
Author(s):  
Pavan Kumar Reddy Boppidi ◽  
Bharathwaj Suresh ◽  
Ainur Zhussupbekova ◽  
Pranab Biswas ◽  
Daragh Mullarkey ◽  
...  
Keyword(s):  
Resistive Switching ◽  
Spike Rate ◽  
Dependent Plasticity ◽  
Rate Dependent ◽  
Neuromorphic Systems

scholarly journals Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity

2012 ◽  
Vol 108 (2) ◽  
pp. 551-566 ◽  
Author(s):  
Jason F. Hunzinger ◽  
Victor H. Chan ◽  
Robert C. Froemke
Keyword(s):  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Rate Dependent ◽  
Temporal Relationships ◽  
Term Potentiation ◽  
Functional Mechanisms

Studies of spike timing-dependent plasticity (STDP) have revealed that long-term changes in the strength of a synapse may be modulated substantially by temporal relationships between multiple presynaptic and postsynaptic spikes. Whereas long-term potentiation (LTP) and long-term depression (LTD) of synaptic strength have been modeled as distinct or separate functional mechanisms, here, we propose a new shared resource model. A functional consequence of our model is fast, stable, and diverse unsupervised learning of temporal multispike patterns with a biologically consistent spiking neural network. Due to interdependencies between LTP and LTD, dendritic delays, and proactive homeostatic aspects of the model, neurons are equipped to learn to decode temporally coded information within spike bursts. Moreover, neurons learn spike timing with few exposures in substantial noise and jitter. Surprisingly, despite having only one parameter, the model also accurately predicts in vitro observations of STDP in more complex multispike trains, as well as rate-dependent effects. We discuss candidate commonalities in natural long-term plasticity mechanisms.

Threshold-Tunable, Spike-Rate-Dependent Plasticity Originating from Interfacial Proton Gating for Pattern Learning and Memory

2020 ◽  
Vol 12 (6) ◽  
pp. 7833-7839 ◽  
Author(s):  
Zheng Yu Ren ◽  
Li Qiang Zhu ◽  
Yan Bo Guo ◽  
Ting Yu Long ◽  
Fei Yu ◽  
...  
Keyword(s):  
Learning And Memory ◽  
Spike Rate ◽  
Pattern Learning ◽  
Dependent Plasticity ◽  
Rate Dependent

Revisiting Memristor Properties

2020 ◽  
Vol 30 (12) ◽  
pp. 2050172
Author(s):  
Ling Chen ◽  
Zhilong He ◽  
Chuandong Li ◽  
Shiping Wen ◽  
Yiran Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Experience ◽  
Time Dependent ◽  
Long Term Memory ◽  
Term Memory ◽  
Dependent Plasticity ◽  
Memory Property ◽  
Rate Dependent

Memristor is a natural synapse because of its nanoscale and memory property, which influences the performance of memristive artificial neural networks. A three-variable memristor model is simplified with 15 kinds of properties, including the learning experience, the forgetting curve, the spiking time-dependent plasticity (STDP), the spiking rate dependent plasticity (SRDP), and the integration property. Through the analysis of the model, one more unobserved property called pseudo-polarity reversibility property is predicted by assuming the long-term memory is independent of memductance.

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