Abstract
Neural coding is one of the central questions in neuroscience for converting visual information into spike patterns. However, the existing encoding techniques require a preset time window and lack effective learning. In order to overcome these two problems, we design an adaptive convolutional auto-encoder based on spiking neurons in this paper. We first exploit the spike pixel mapping decoding approach to find the optimal value of the time window automatically. Next, we design a deep convolutional neural network to adapt the learning parameters by reconstruction errors to realize the spike encoding process. Then we can naturally get coding pre-training parameters for unifying the convolutional spike coding layer with back-end deep spiking neural networks (SNNs) for recognition tasks. Simulation results demonstrate that the proposed method can achieve better performance compared with other encoding methods.
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This work was supported by the National Natural Science Foundation of China NSAF under Grant No. U2030204.
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Zhu, C., Jiang, J., Jiang, R., Yan, R. (2023). An Adaptive Convolution Auto-encoder Based on Spiking Neurons. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13624. Springer, Cham. https://doi.org/10.1007/978-3-031-30108-7_5
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