Walters, Ben, Cai, Zhengyu, Kalatehbali, Hamid Rahimian, Amirsoleimani, Amirali, Genov, Roman, Eshraghian, Jason, and Rahimi Azghadi, Mostafa (2024) Spiking Auto-Encoder Using Error Modulated Spike Timing Dependant Plasticity. In: Proceedings of the IEEE International Symposium on Circuits and Systems. From: ISCAS 2024: IEEE International Symposium on Circuits and Systems, 19-22 May 2024, Singapore.

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Abstract

Auto-encoders are capable of performing input re-construction through an encoder-decoder structure. These net-works can serve many purposes such as noise removal and anomaly detection, whilst being trained without the need for labelled data. Spiking auto-encoders can utilise asynchronous spikes to potentially improve power and simplify the required hardware. In this work, we propose an efficient spiking auto-encoder with novel error-modulated STDP learning. Our auto-encoder uses the Time To First Spike (TTFS) encoding scheme and needs to update all synaptic weights only once per input. Also, it needs only an average of 8 spikes in its hidden layer for reconstruction, leading to a very sparse and hence potentially power-efficient implementation. We demonstrate decent reconstruction ability for MNIST and the challenging Caltech Face/Motorbike datasets and achieve excellent noise removal from MNIST images.

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