Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks

Azouz R, Gray CM (2000) Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo. Proc Natl Acad Sci 97(14):8110–8115

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bao B, Hu J, Bao H, et al (2023a) Memristor-coupled dual-neuron mapping model: initials-induced coexisting firing patterns and synchronization activities. Cognit Neurodyn pp 1–17

Bao H, Yu X, Xu Q et al (2023) Three-dimensional memristive morris-lecar model with magnetic induction effects and its fpga implementation. Cogn Neurodyn 17(4):1079–1092

Article  PubMed  Google Scholar 

Bu T, Fang W, Ding J, et al (2023) Optimal ann-snn conversion for high-accuracy and ultra-low-latency spiking neural networks. arXiv preprint arXiv:2303.04347

Chen T, Wang L, Duan S (2020) Implementation of circuit for reconfigurable memristive chaotic neural network and its application in associative memory. Neurocomputing 380:36–42

Article  Google Scholar 

Cheng Y, Wang D, Zhou P, et al (2017) A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282

Davies M, Srinivasa N, Lin TH et al (2018) Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1):82–99

Article  Google Scholar 

Deco G, Cruzat J, Kringelbach ML (2019) Brain songs framework used for discovering the relevant timescale of the human brain. Nat Commun 10(1):583

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deng S, Li Y, Zhang S, et al (2022) Temporal efficient training of spiking neural network via gradient re-weighting. arXiv preprint arXiv:2202.11946

Diehl PU, Cook M (2015) Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front Comput Neurosci 9

Fang W, Yu Z, Chen Y et al (2021) Deep residual learning in spiking neural networks. Adv Neural Inf Process Syst 34:21056–21069

Google Scholar 

Fang W, Yu Z, Chen Y, et al (2021b) Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 2661–2671

Fang X, Liu D, Duan S et al (2022) Memristive lif spiking neuron model and its application in morse code. Front Neurosci 16:374

Google Scholar 

Furber SB, Galluppi F, Temple S et al (2014) The spinnaker project. Proc IEEE 102(5):652–665

Article  Google Scholar 

Gerstner W, Kistler WM, Naud R, et al (2014) Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge University Press

Guo Y, Tong X, Chen Y, et al (2022) Recdis-snn: rectifying membrane potential distribution for directly training spiking neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 326–335

Han B, Srinivasan G, Roy K (2020) Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 13558–13567

He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

He W, Wu Y, Deng L et al (2020) Comparing snns and rnns on neuromorphic vision datasets: similarities and differences. Neural Netw 132:108–120

Article  PubMed  Google Scholar 

Herranz-Celotti L, Rouat J (2022) Surrogate gradients design. arXiv preprint arXiv:2202.00282

Hu Y, Wu Y, Deng L, et al (2021) Advancing residual learning towards powerful deep spiking neural networks. arXiv preprint arXiv:2112.08954

Kheradpisheh SR, Ganjtabesh M, Thorpe SJ et al (2018) Stdp-based spiking deep convolutional neural networks for object recognition. Neural Netw 99:56–67

Article  PubMed  Google Scholar 

Kheradpisheh SR, Ganjtabesh M, Thorpe SJ et al (2018) Stdp-based spiking deep convolutional neural networks for object recognition. Neural Netw 99:56–67

Article  PubMed  Google Scholar 

Kim T, Hu S, Kim J et al (2021) Spiking neural network (snn) with memristor synapses having non-linear weight update. Front Comput Neurosci 15:646125

Article  PubMed  PubMed Central  Google Scholar 

Lee C, Sarwar SS, Panda P, et al (2020) Enabling spike-based backpropagation for training deep neural network architectures. Front Neurosci 119

Lee JH, Delbruck T, Pfeiffer M (2016) Training deep spiking neural networks using backpropagation. Front Neurosci 10:508

Article  PubMed  PubMed Central  Google Scholar 

Li D, Chen X, Becchi M, et al (2016) Evaluating the energy efficiency of deep convolutional neural networks on cpus and gpus. In: 2016 IEEE international conferences on big data and cloud computing (BDCloud), social computing and networking (SocialCom), sustainable computing and communications (SustainCom)(BDCloud-SocialCom-SustainCom), IEEE, pp 477–484

Li J, Zhou G, Li Y et al (2022) Reduction 93.7% time and power consumption using a memristor-based imprecise gradient update algorithm. Artif Intell Rev 55(1):657–677

Article  Google Scholar 

Li Y, Deng S, Dong X, et al (2021a) A free lunch from ann: Towards efficient, accurate spiking neural networks calibration. In: International conference on machine learning, PMLR, pp 6316–6325

Li Y, Guo Y, Zhang S et al (2021) Differentiable spike: rethinking gradient-descent for training spiking neural networks. Adv Neural Inf Process Syst 34:23426–23439

Google Scholar 

Li Y, Kim Y, Park H, et al (2022b) Neuromorphic data augmentation for training spiking neural networks. In: European conference on computer vision, Springer, pp 631–649

Lian S, Shen J, Liu Q, et al (2023) Learnable surrogate gradient for direct training spiking neural networks. In: Proceedings of the thirty-second international joint conference on artificial intelligence, IJCAI-23, pp 3002–3010

Lin H, Wang C, Sun Y et al (2020) Firing multistability in a locally active memristive neuron model. Nonlinear Dyn 100(4):3667–3683

Article  Google Scholar 

Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

Mattia M, Del Giudice P (2002) Population dynamics of interacting spiking neurons. Phys Rev E 66(5):051917

Article  Google Scholar 

Neftci EO, Mostafa H, Zenke F (2019) Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process Mag 36(6):51–63

Article  Google Scholar 

Özçelik YB, Altan A (2023) Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust ai-based model with chaotic swarm intelligence optimization and recurrent long short-term memory. Fractal and Fractional 7(8):598

Article  Google Scholar 

Pei J, Deng L, Song S et al (2019) Towards artificial general intelligence with hybrid tianjic chip architecture. Nature 572(7767):106–111

Article  CAS  PubMed  Google Scholar 

Rathi N, Roy K (2020) Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks. arXiv preprint arXiv:2008.03658

Rathi N, Srinivasan G, Panda P, et al (2020) Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation. arXiv preprint arXiv:2005.01807

Redmon J, Divvala S, Girshick R, et al (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788

Roy K, Jaiswal A, Panda P (2019) Towards spike-based machine intelligence with neuromorphic computing. Nature 575(7784):607–617

Article  CAS  PubMed  Google Scholar 

Sengupta A, Ye Y, Wang R et al (2019) Going deeper in spiking neural networks: Vgg and residual architectures. Front Neurosci 13:95

Article  PubMed  PubMed Central  Google Scholar 

Shrestha SB, Orchard G (2018) Slayer: Spike layer error reassignment in time. Advances in neural information processing systems 31

Sun H, Cai W, Yang B, et al (2023) A synapse-threshold synergistic learning approach for spiking neural networks. IEEE Trans Cognitive Dev Syst

Tavanaei A, Ghodrati M, Kheradpisheh SR et al (2019) Deep learning in spiking neural networks. Neural Netw 111:47–63

Article  PubMed  Google Scholar 

Wu Y, Deng L, Li G et al (2018) Spatio-temporal backpropagation for training high-performance spiking neural networks. Front Neurosci 12:331

Article  PubMed  PubMed Central  Google Scholar 

Xie Y, Ye Z, Li X, et al (2024) A novel memristive neuron model and its energy characteristics. Cognit Neurodyn pp 1–13

Xu Q, Ju Z, Ding S et al (2022) Electromagnetic induction effects on electrical activity within a memristive wilson neuron model. Cogn Neurodyn 16(5):1221–1231

Article  PubMed  PubMed Central  Google Scholar 

Yağ İ, Altan A (2022) Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments. Biology 11(12):1732

Article  PubMed  PubMed Central  Google Scholar 

Yao X, Li F, Mo Z, et al (2022) Glif: A unified gated leaky integrate-and-fire neuron for spiking neural networks. arXiv preprint arXiv:2210.13768

Zhang T, Jia S, Cheng X et al (2021) Tuning convolutional spiking neural network with biologically plausible reward propagation. IEEE Trans Neural Netw Learn Syst 33(12):7621–7631

Article  Google Scholar 

Zhao Z, Qu L, Wang L et al (2020) A memristor-based spiking neural network with high scalability and learning efficiency. IEEE Trans Circuits Syst II Express Briefs 67(5):931–935

Google Scholar 

Zheng H, Wu Y, Deng L, et al (2021) Going deeper with directly-trained larger spiking neural networks. In: Proceedings of the AAAI conference on artificial intelligence, pp 11062–11070

Zhou G, Ren Z, Wang L et al (2019) Artificial and wearable albumen protein memristor arrays with integrated memory logic gate functionality. Mater Horiz 6(9):1877–1882

Article  CAS  Google Scholar 

Zhou G, Ji X, Li J et al (2022) Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory. Iscience 25(10):105240

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou G, Wang Z, Sun B et al (2022) Volatile and nonvolatile memristive devices for neuromorphic computing. Adv Electron Mater 8(7):2101127

Article  CAS  Google Scholar 

Zhu RJ, Zhang M, Zhao Q, et al (2024) Tcja-snn: Temporal-channel joint attention for spiking neural networks. IEEE Trans Neural Netw Learn Syst

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