Exploiting Synaptic Traces for Online Weight Adaptation in Spiking Autoencoder Networks

Spiking Neural Networks (SNNs) represent a novel class of models that are becoming increasingly popular. Thanks to their unique way to process discrete and asynchronous signals, these type of networks demonstrated to be computationally and energetically more efficient than traditional Deep Neural Networks (DNNs), making them suitable for devices with limited hardware capabilities. Moreover, their ability to continuously adapt to external inputs changes enables an online evolution of network weights which is ideal for real-time applications scenarios. As a downside, one of the main challenges when working with SNNs consists in the impossibility of using traditional learning rules such as backpropagation (or its variations) for the online training of these architectures. In such a context emerges the need to define online and biologically plausible training solutions that would allow the hardware implementation of these networks on neuromorphic devices. To overcome this issue, in this paper we propose a learning rule for the training of vanilla Spiking Autoencoders (Spiking-AEs) which exploits synaptic trace activities to enable an online weights adaptation. We also propose a novel modular tied Spiking-AE to address reconstruction and anomaly detection tasks. In this sense, the tied connections make the training less complex, drastically reducing the number of trainable parameters and model memory footprint. Experimental results conducted on the MNIST and MFPT datasets demonstrate the effectiveness of the proposed solution in terms of reconstruction and anomaly detection capabilities, respectively.