A Highly Effective and Robust Membrane Potential-Driven Supervised Learning Method for Spiking Neurons

Zhang, Malu, Qu, Hong, Belatreche, Ammar, Chen, Yi and Yi, Zhang (2019) A Highly Effective and Robust Membrane Potential-Driven Supervised Learning Method for Spiking Neurons. IEEE Transactions on Neural Networks and Learning Systems, 30 (1). pp. 123-137. ISSN 2162-237X

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accepted and revised version_accepted 22-Apr-2018_in press_A Highly Effective and Robust Membrane Potential Driven Supervised Learning Method for Spiking Neurons.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1109/TNNLS.2018.2833077

Abstract

Spiking neurons are becoming increasingly popular owing to their biological plausibility and promising computational properties. Unlike traditional rate-based neural models, spiking neurons encode information in the temporal patterns of the transmitted spike trains, which makes them more suitable for processing spatiotemporal information. One of the fundamental computations of spiking neurons is to transform streams of input spike trains into precisely timed firing activity. However, the existing learning methods, used to realize such computation, often result in relatively low accuracy performance and poor robustness to noise. In order to address these limitations, we propose a novel highly effective and robust membrane potential-driven supervised learning (MemPo-Learn) method, which enables the trained neurons to generate desired spike trains with higher precision, higher efficiency, and better noise robustness than the current state-of-the-art spiking neuron learning methods. While the traditional spike-driven learning methods use an error function based on the difference between the actual and desired output spike trains, the proposed MemPo-Learn method employs an error function based on the difference between the output neuron membrane potential and its firing threshold. The efficiency of the proposed learning method is further improved through the introduction of an adaptive strategy, called skip scan training strategy, that selectively identifies the time steps when to apply weight adjustment. The proposed strategy enables the MemPo-Learn method to effectively and efficiently learn the desired output spike train even when much smaller time steps are used. In addition, the learning rule of MemPo-Learn is improved further to help mitigate the impact of the input noise on the timing accuracy and reliability of the neuron firing dynamics. The proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. Experimental results show that the proposed method can achieve high learning accuracy with a significant improvement in learning time and better robustness to different types of noise.

Item Type: Article
Uncontrolled Keywords: Classification, gradient descent, spiking neural networks, spiking neurons, supervised learning.
Subjects: G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 13 Jun 2018 12:10
Last Modified: 11 Jan 2019 13:15
URI: http://nrl.northumbria.ac.uk/id/eprint/34520

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