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STDP Based Online Learning for a Current-Controlled Memristive Synapse
Midwest Symposium on Circuits and Systems, 2022Spike-timing-dependent plasticity (STDP) is a popular approach for online learning that determines synaptic weight updates based on the relative timing of temporal events of pre-synaptic and post-synaptic spikes.
Ryan Weiss +3 more
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A Fast Spiking Neural Network Accelerator based on BP-STDP Algorithm and Weighted Neuron Model
IEEE Transactions on Circuits and Systems - II - Express Briefs, 2022Spiking neural networks (SNNs) are inspired from biological brains and have demonstrated great energy efficiency on hardware computing platforms. However, it is a challenge to implement an online training algorithm on SNN hardware to adapt to the ...
Jian Zhang +5 more
semanticscholar +1 more source
Neural Computation, 2003
We demonstrate that the BCM learning rule follows directly from STDP when pre- and postsynaptic neurons fire uncorrelated or weakly correlated Poisson spike trains, and only nearest-neighbor spike interactions are taken into account.
Niraj S. Desai, Eugene M. Izhikevich
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We demonstrate that the BCM learning rule follows directly from STDP when pre- and postsynaptic neurons fire uncorrelated or weakly correlated Poisson spike trains, and only nearest-neighbor spike interactions are taken into account.
Niraj S. Desai, Eugene M. Izhikevich
openaire +3 more sources
Phase Precession and Recession with STDP and Anti-STDP [PDF]
, 2006 We show that standard, Hebbian spike-timing dependent plasticity (STDP) induces the precession of the firing phase of neurons in oscillatory networks, while anti-Hebbian STDP induces phase recession. In networks that are subject to oscillatory inhibition, the intensity of excitatory input relative to the inhibitory one determines whether the phase can ...
Raul C. Mureşan, Răzvan V. Florian
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IEEE Sensors Journal, 2022
The inherent spike-based and event-driven computation makes spiking neural networks (SNNs) naturally suitable to provide efficient and low-latency solution in neuromorphic vision processing.
Qian Zhou, Xiaohu Li
semanticscholar +1 more source
The inherent spike-based and event-driven computation makes spiking neural networks (SNNs) naturally suitable to provide efficient and low-latency solution in neuromorphic vision processing.
Qian Zhou, Xiaohu Li
semanticscholar +1 more source
STDP learning rule based on memristor with STDP property
2014 International Joint Conference on Neural Networks (IJCNN), 2014Spike-timing-dependent plasticity (STDP) learning ability has been observed in physical memristors, but whether the STDP is caused by the neuron or the memristor is unclear. In this paper, we proved the STDP property in the model for both symmetric and asymmetric memristor. We also employed the symmetric/asymmetric memristors with STDP property and the
Hai Li +5 more
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Memristor-Based In-Circuit Computation for Trace-Based STDP
International Conference on Artificial Intelligence Circuits and Systems, 2022Recently, memristors have been widely used to implement Spiking Neural Networks (SNNs), which is promising in edge computing scenarios. However, most memristor-based SNN implementations adopt simplified spike-timing-dependent plasticity (STDP) for the ...
Deyuan Wang +8 more
semanticscholar +1 more source
Fine-tuning Deep Reinforcement Learning Policies with r-STDP for Domain Adaptation
International Conference on Systems, 2022Using deep reinforcement learning policies that are trained in simulation on real robotic platforms requires fine-tuning due to discrepancies between simulated and real environments.
Mahmoud Akl +4 more
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STDP-Driven Development of Attention-Based People Detection in Spiking Neural Networks
IEEE Transactions on Cognitive and Developmental SystemsThis letter provides, to the best of our knowledge, a first analysis of how biologically plausible spiking neural networks (SNNs) equipped with spike-timing-dependent plasticity (STDP) can learn to detect people on the fly from nonindependent and ...
A. Safa +5 more
semanticscholar +1 more source
Efficient Design of Spiking Neural Network With STDP Learning Based on Fast CORDIC
IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 2021In emerging Spiking Neural Network (SNN) based neuromorphic hardware design, energy efficiency and on-line learning are attractive advantages mainly contributed by bio-inspired local learning with nonlinear dynamics and at the cost of associated hardware
Jiajun Wu +6 more
semanticscholar +1 more source