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Spike timing dependent plasticity with memristive synapse in neuromorphic systems

William Chan, Jason Lohn
2012 The 2012 International Joint Conference on Neural Networks (IJCNN)  
A methodology to realize spike-timing dependent plasticity and Hebbian learning in a neural network through the usage of memristive synapses is presented.  ...  Memristors act as a modulating synapse interconnection between neurons; plasticity is accomplished through adjusting the memristance via current spikes based on the relative timings of pre-synaptic and  ...  CONCLUSION In conclusion, we have presented a novel CMOS circuit design demonstrating spike-timing dependent plasticity with memristive synapse.  ... 
doi:10.1109/ijcnn.2012.6252822 dblp:conf/ijcnn/ChanL12 fatcat:setacxtuurc4nbljl47js672bi

Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems

Yi Li, Yingpeng Zhong, Jinjian Zhang, Lei Xu, Qing Wang, Huajun Sun, Hao Tong, Xiaoming Cheng, Xiangshui Miao
2014 Scientific Reports  
The memristive characteristics with reproducible gradual resistance tuning are utilised to mimic the activity-dependent synaptic plasticity that serves as the basis of memory and learning.  ...  Activity-dependent synaptic plasticity is fundamental for learning and memory in neuronal systems involving information processing and storage.  ...  We see the implementation of activity-dependent synaptic plasticity in an electronic synapse as a solid step toward constructing neuromorphic systems, but these results still call for additional research  ... 
doi:10.1038/srep04906 pmid:24809396 pmcid:PMC4014880 fatcat:q5vqyuxggfd3djcyimvgoutzba

Plasticity in memristive devices for spiking neural networks

Sylvain Saïghi, Christian G. Mayr, Teresa Serrano-Gotarredona, Heidemarie Schmidt, Gwendal Lecerf, Jean Tomas, Julie Grollier, Sören Boyn, Adrien F. Vincent, Damien Querlioz, Selina La Barbera, Fabien Alibart (+4 others)
2015 Frontiers in Neuroscience  
Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering.  ...  Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering.  ...  Based on this different coding strategies, variations of Hebbian learning have been proposed such has Spike Rate Dependent Plasticity (SRDP) or the very popular Spike Timing Dependent Plasticity (STDP)  ... 
doi:10.3389/fnins.2015.00051 pmid:25784849 pmcid:PMC4345885 fatcat:xvlfsjqnbjgvzhro7rigmhsdfm

Perspective on photonic memristive neuromorphic computing

Elena Goi, Qiming Zhang, Xi Chen, Haitao Luan, Min Gu
2020 PhotoniX  
Current electronic implementations of neuromorphic architectures are still far from competing with their biological counterparts in terms of real-time information-processing capabilities, packing density  ...  This new field combines the advantages of photonics and neuromorphic architectures to build systems with high efficiency, high interconnectivity and high information density, and paves the way to ultrafast  ...  MG conceived the present idea, QZ performed the calculations reported in Fig. 10 and EG wrote the paper with input from all authors. All authors read and approved the final manuscript.  ... 
doi:10.1186/s43074-020-0001-6 fatcat:qvhwulx7ova4lnijjhtwnpunqe

CMOS and Memristive Hardware for Neuromorphic Computing

Mostafa Rahimi Azghadi, Ying-Chen Chen, Jason K. Eshraghian, Jia Chen, Chih-Yang Lin, Amirali Amirsoleimani, Adnan Mehonic, Anthony J Kenyon, Burt Fowler, Jack C. Lee, Yao-Feng Chang
2020 Advanced Intelligent Systems  
Saber Moradi and Professor Indiveri for designing the circuit in Figure 3a and the chip, where its architecture is shown in Figure 11a .  ...  Figure 2 and conduct the experiments shown in Figure 11 .  ...  [30, 31] The circuit that is shown in Figure 2a has been designed and fabricated in CMOS to implement the triplet spike timing-dependent plasticity (STDP) rule proposed in a previous study.  ... 
doi:10.1002/aisy.201900189 fatcat:lrspxweqlfb6bmmqir6mwzvx44

Implementation of a spike-based perceptron learning rule using TiO2−x memristors

Hesham Mostafa, Ali Khiat, Alexander Serb, Christian G. Mayr, Giacomo Indiveri, Themis Prodromakis
2015 Frontiers in Neuroscience  
We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules.  ...  Neuromorphic systems need to implement plastic synapses to obtain basic "cognitive" capabilities such as learning.  ...  plasticity rules in that it does not explicitly depend on the precise timing of both pre-and postsynaptic neuron spikes.  ... 
doi:10.3389/fnins.2015.00357 pmid:26483629 pmcid:PMC4591430 fatcat:7iqu3uxnuzg6hg3g2l7om3npdi

STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks

Ahmad Afifi, Ahmad Ayatollahi, Farshid Raissi
2009 IEICE Electronics Express  
Implementation of a correlation-based learning rule, Spike-Timing-Dependent-Plasticity (STDP), for asynchronous neuromorphic networks is demonstrated using 'memristive' nanodevice.  ...  The performance of the proposed method is analyzed for specifically shaped spikes and simulation results are provided for a synapse with STDP properties.  ...  Structural view of CMOL neuromorphic networks using memristive nanodevices with nonlinear conductance (g).  ... 
doi:10.1587/elex.6.148 fatcat:hhe2lkfp3rfr5o46jiga6ccym4

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

Jiajuan Shi, Zhongqiang Wang, Ye Tao, Haiyang Xu, Xiaoning Zhao, Ya Lin, Yichun Liu
2021 Frontiers in Neuroscience  
In this review, we give a systematic description of self-powered memristive systems from storage to neuromorphic computing.  ...  The review also proves a perspective on the application of artificial intelligence with the self-powered memristive system.  ...  As is shown in Figure 1b -i, by using an NKN NG to provide priming spikes, they have regulated the spike-time-dependent plasticity (STDP) in the NKN memristor.  ... 
doi:10.3389/fnins.2021.662457 pmid:33867930 pmcid:PMC8044301 fatcat:u5spdpicxfb7xo5ue52e3llxha

A compound memristive synapse model for statistical learning through STDP in spiking neural networks

Johannes Bill, Robert Legenstein
2014 Frontiers in Neuroscience  
Using this abstract model, we first show how standard pulsing schemes give rise to spike-timing dependent plasticity (STDP) with a stabilizing weight dependence in compound synapses.  ...  In a next step, we study unsupervised learning with compound synapses in networks of spiking neurons organized in a winner-take-all architecture.  ...  ACKNOWLEDGMENTS We thank Radu Berdan, Alex Serb and Themis Prodromakis for valuable discussions on memristors, Giacomo Indiveri for initial discussions on the compound memristive synapse model, Georg Goeri  ... 
doi:10.3389/fnins.2014.00412 pmid:25565943 pmcid:PMC4267210 fatcat:kbaoyd665bcwtox6zpnhkkzwc4

Low-Power Neuromorphic Hardware for Signal Processing Applications [article]

Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa,, Evangelos Eleftheriou
2019 arXiv   pre-print
Hence, novel computational architectures that address the von Neumann bottleneck are necessary in order to build systems that can implement SNNs with low energy budgets.  ...  In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information  ...  Fundamental principles of synaptic changes have been uncovered that depend on the timing of spikes fired in the pre-and postsynaptic cells, thus termed Spike-Timing Dependent Plasticity or STDP [12] .  ... 
arXiv:1901.03690v3 fatcat:34eavryprvdaxcvuteujwizeia

A geographically distributed bio-hybrid neural network with memristive plasticity [article]

Alexantrou Serb, Andrea Corna, Richard George, Ali Khiat, Federico Rocchi, Marco Reato, Marta Maschietto, Chirstian Mayr, Giacomo Indiveri, Stefano Vassanelli, Themistoklis Prodromakis
2017 arXiv   pre-print
Electronics has made important steps in emulating neurons through neuromorphic circuits and synapses with nanoscale memristors, yet novel applications that interlink them in heterogeneous bio-inspired  ...  Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with memory storage and computation.  ...  The rather infrequent activity observed at BN caused the spike rate-dependent plasticity (SRDP) plasticity BCM rule to lead to strong LTD at the reverse pathway synapse and create the LTD/none/LTD plasticity  ... 
arXiv:1709.04179v1 fatcat:cevynk4ww5c2xngiyc4hvzdy5u

Energy-efficient STDP-based learning circuits with memristor synapses

Xinyu Wu, Vishal Saxena, Kristy A. Campbell, Misty Blowers, Jonathan Williams
2014 Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII  
Instead, we employ bio-inspired learning rules such as the spike-timing-dependent plasticity (STOP) to efficiently update the network weights locally.  ...  Novel IFN circuits have been designed to drive memristive synapses in parallel while maintaining overall power efficiency (<I pJ/spike/synapse), even at spike rate greater than 10 MHz.  ...  In particular, compatible with this structure is the weight setting paradigm spike-timing-dependent plasticity (STDP) 5 • STDP is an important synaptic modification rule for competitive Hebbian learning  ... 
doi:10.1117/12.2053359 fatcat:roogwyquczdflnurouguzcxocy

Bottleneck of using single memristor as a synapse and its solution [article]

Farnood Merrikh-Bayat, Saeed Bagheri Shouraki, Iman Esmaili Paeen Afrakoti
2012 arXiv   pre-print
This phenomenon can degrade the performance of learning methods like Spike Timing-Dependent Plasticity (STDP) and cause the corresponding neuromorphic systems to become unstable.  ...  It is now widely accepted that memristive devices are perfect candidates for the emulation of biological synapses in neuromorphic systems.  ...  support important synaptic functions such as Spike Timing Dependent Plasticity (STDP) [3] .  ... 
arXiv:1008.3450v3 fatcat:ojukhuqmknf5fk5tgzzbdwnssy

Flexible and Transparent Memristive Synapse Based on Polyvinylpyrrolidone/N-doped Carbon Quantum Dot Nanocomposites for Neuromorphic Computing

Tao Zeng, Zhi Yang, Jiabing Liang, Ya Lin, Yankun Cheng, Xiaochi Hu, Xiaoning Zhao, Zhongqiang Wang, Haiyang Xu, Yichun Liu
2021 Nanoscale Advances  
Memristive devices are widely recognized as promising hardware implementations of neuromorphic computing.  ...  Herein, a flexible and transparent memristive synapse based on polyvinylpyrrolidone (PVP)/N-doped carbon quantum dot (NCQD) nanocomposites through regulating...  ...  In neuromorphic systems, the spike-timing-dependent plasticity (STDP) learning rules are one typical type of LTP, which is one of the essential learning laws for emulating synaptic functions. 49,50 By  ... 
doi:10.1039/d1na00152c fatcat:asboxin26rbhfkyj4hfnxwdx5y

A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems

Zhongqiang Wang, Stefano Ambrogio, Simone Balatti, Daniele Ielmini
2015 Frontiers in Neuroscience  
Spike timing dependent plasticity (STDP) is achieved through appropriate shaping of the pre-synaptic and the post synaptic spikes.  ...  Keywords: neuromorphic circuits, spike timing dependent plasticity, neural network, memristor, pattern recognition, cognitive computing January 2015 | Volume 8 | Article 438 | 1 Wang  ...  The synchronous approach, however, may be too idealized with respect to the biological brain, where synapses are potentiated/depressed through asynchronous spike timing dependent plasticity (STDP) (Bi  ... 
doi:10.3389/fnins.2014.00438 pmid:25642161 pmcid:PMC4295533 fatcat:sx6zqhgbgvd23i35sq7po4rnvu
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