ArticleFrontiers in computational neuroscience2021
Spiking Neural Network (SNN) With Memristor Synapses Having Non-linear Weight Update.
Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Polymer-Based Linear and Symmetric Artificial Synaptic Memristors for Accurate and Reliable Neuromorphic Computing Applications.Nanomaterials (Basel, Switzerland) · 2026Review
- Article
- A Study on the Synaptic Behavior of Al/ZrOACS applied materials & interfaces · 2025Article
- Defect-Tolerant Memristor Crossbar Circuits for Local Learning Neural Networks.Nanomaterials (Basel, Switzerland) · 2025Article
- Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks.Cognitive neurodynamics · 2024Article
- Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.Frontiers in neuroscience · 2024Article
- Memristor Crossbar Circuits Implementing Equilibrium Propagation for On-Device Learning.Micromachines · 2023Article
- Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Dynamical memristive neural networks and associative self-learning architectures using biomimetic devices.Frontiers in neuroscience · 2023Article
- Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm.Frontiers in neuroscience · 2021Article
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Authors and funding
9 authors.
Funding
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Abstract
Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor is a promising candidate as a synaptic component for hardware implementation of SNN, but several non-ideal device properties are making it challengeable. In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance. We found that SNN has a strong tolerance for the device non-linearity and the network can keep the accuracy high if a device meets one of the two conditions: 1. symmetric LTP and LTD curves and 2. positive non-linearity factors for both LTP and LTD. The reason was analyzed in terms of the balance between network parameters as well as the variability of weight. The results are considered to be a piece of useful prior information for the future implementation of emerging device-based neuromorphic hardware.
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