Faculty Opinions recommendation of STDP-based spiking deep convolutional neural networks for object recognition.

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RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network (2020, methodology): “STDP-based unsupervised [5, 23, 37, 40] and semi-supervised learning algorithms [17, 20, 25, 39] have thus far been restricted to shallow SNNs (with ≤ 5 layers) yielding considerably lower than acceptable accuracy on complex datasets like CIFAR-10 [9, 38].”
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks (2019, methodology): “Furthermore, there are biologically inspired online approximations of surrogate gradients [2], [73], [74] and, finally, a body of work has used biologically motivated spiketiming dependent plasticity (STDP)-like learning rules [75], [76] (see [4] for a comprehensive review).”
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks (2021, neutral): “The ALIF neuron is similarly modeled as : ut = αut−1 +(1−α)RmIt −θSt−1 (5) ηt = ρηt−1 +(1−ρ)St−1 (6) θ = b0 +βηt (7) St = f̂s(ut ,θ), (8)”
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks (2020, methodology): “Existing approaches exploited the self-organization principle [56, 11, 29], and STDP-based expectation-maximization algorithm [43, 17].”