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Spike timing dependent plasticity with memristive synapse in neuromorphic systems
2012
The 2012 International Joint Conference on Neural Networks (IJCNN)
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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
2014
Scientific Reports
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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
2015
Frontiers in Neuroscience
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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
2020
PhotoniX
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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
2020
Advanced Intelligent Systems
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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
2015
Frontiers in Neuroscience
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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
2009
IEICE Electronics Express
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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
2021
Frontiers in Neuroscience
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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
2014
Frontiers in Neuroscience
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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]
2019
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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
2014
Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII
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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]
2012
arXiv
pre-print
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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
2021
Nanoscale Advances
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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
2015
Frontiers in Neuroscience
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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 www.frontiersin.org 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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