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Spike time displacement-based error backpropagation in convolutional spiking neural networks

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Abstract

In this paper, we introduce a supervised learning algorithm, which avoids backward recursive gradient computation, for training deep convolutional spiking neural networks (SNNs) with single-spike-based temporal coding. The algorithm employs a linear approximation to compute the derivative of the spike latency with respect to the membrane potential, and it uses spiking neurons with piecewise linear postsynaptic potential to reduce the computational cost and the complexity of neural processing. To evaluate the performance of the proposed algorithm in deep architectures, we employ it in convolutional SNNs for the image classification task. For two popular benchmarks of MNIST and Fashion-MNIST datasets, the network reaches accuracies of, respectively, 99.2 and \(92.8\%\). The trade-off between memory storage capacity and computational cost with accuracy is analyzed by applying two sets of weights: real-valued weights that are updated in the backward pass and their signs, binary weights, that are employed in the feedforward process. We evaluate the binary CSNN on two datasets of MNIST and Fashion-MNIST and obtain acceptable performance with a negligible accuracy drop with respect to real-valued weights (about 0.6 and \(0.8\%\) drops, respectively).

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Data Availability

The codes generated during the current study are available in the mmirsadeghi repository, at https://github.com/mmirsadeghi/StiDi-BP.

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Authors and Affiliations

  1. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

    Maryam Mirsadeghi & Majid Shalchian

  2. Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

    Saeed Reza Kheradpisheh

  3. CerCo UMR 5549, CNRS - Université Toulouse 3, Toulouse, France

    Timothée Masquelier

Authors
  1. Maryam Mirsadeghi
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  2. Majid Shalchian
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  3. Saeed Reza Kheradpisheh
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  4. Timothée Masquelier
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Correspondence to Majid Shalchian.

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Mirsadeghi, M., Shalchian, M., Kheradpisheh, S.R. et al. Spike time displacement-based error backpropagation in convolutional spiking neural networks. Neural Comput & Applic 35, 15891–15906 (2023). https://doi.org/10.1007/s00521-023-08567-0

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  • DOI: https://doi.org/10.1007/s00521-023-08567-0

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