AP-STDP: A novel self-organizing mechanism for efficient reservoir computing

The Liquid State Machine (LSM) exploits the computation capability of recurrent spiking neural networks by incorporating a randomly generated reservoir, which is often fixed. This standard choice relaxes the challenging need for training the complex recurrent reservoir. The fixed reservoir is used as a generic kernel to map the temporal input signals to the internal network dynamics, and a readout layer is trained to extract the information embedded in the network dynamics to facilitate pattern classification. However, the question of how to effectively tune the reservoir for given computational tasks remains to be answered. In this paper, we propose a novel Activity-based Probabilistic Spiking-Timing Dependent Plastic (AP-STDP) mechanism for self-organizing reservoirs. Compared to conventional STDP mechanisms, the proposed rule improves tuning efficiency, prevents the saturation of synaptic memory, and boosts performance. We assess the internal representation ability of the proposed self-organizing mechanism via principal component analysis (PCA) and show that the proposed method is advantageous over other STDP algorithms. Using the spoken English letters adopted from the TI46 speech corpus for performance benchmarking, we demonstrate that AP-STDP consistently outperforms other STDP mechanisms regardless of reservoir size, and is able to boost the performance of the isolated spoken English letter recognition by 2.7% with a small reservoir size.