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A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons

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      Abstract

      Neuroscience research confirms that the synaptic delays are not constant, but can be modulated. This paper proposes a supervised delay learning algorithm for spiking neurons with temporal encoding, in which both the weight and delay of a synaptic connection can be adjusted to enhance the learning performance. The proposed algorithm firstly defines spike train kernels to transform discrete spike trains during the learning phase into continuous analog signals so that common mathematical operations can be performed on them, and then deduces the supervised learning rules of synaptic weights and delays by gradient descent method. The proposed algorithm is successfully applied to various spike train learning tasks, and the effects of parameters of synaptic delays are analyzed in detail. Experimental results show that the network with dynamic delays achieves higher learning accuracy and less learning epochs than the network with static delays. The delay learning algorithm is further validated on a practical example of an image classification problem. The results again show that it can achieve a good classification performance with a proper receptive field. Therefore, the synaptic delay learning is significant for practical applications and theoretical researches of spiking neural networks.

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      Most cited references 46

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      LabelMe: A Database and Web-Based Tool for Image Annotation

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        Networks of spiking neurons: The third generation of neural network models

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          Spike-triggered neural characterization.

          Response properties of sensory neurons are commonly described using receptive fields. This description may be formalized in a model that operates with a small set of linear filters whose outputs are nonlinearly combined to determine the instantaneous firing rate. Spike-triggered average and covariance analyses can be used to estimate the filters and nonlinear combination rule from extracellular experimental data. We describe this methodology, demonstrating it with simulated model neuron examples that emphasize practical issues that arise in experimental situations.
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            Author and article information

            Affiliations
            College of Computer Science and Engineering, Northwest Normal University , Lanzhou, China
            Author notes

            Edited by: Yansong Chua, Institute for Infocomm Research (A *STAR), Singapore

            Reviewed by: Shaista Hussain, Institute of High Performance Computing (A *STAR), Singapore; Liam P. Maguire, Ulster University, United Kingdom

            *Correspondence: Xianghong Lin linxh@ 123456nwnu.edu.cn

            This article was submitted to Neuromorphic Engineering, a section of the journal Frontiers in Neuroscience

            Contributors
            Journal
            Front Neurosci
            Front Neurosci
            Front. Neurosci.
            Frontiers in Neuroscience
            Frontiers Media S.A.
            1662-4548
            1662-453X
            27 March 2019
            2019
            : 13
            6445871
            10.3389/fnins.2019.00252
            Copyright © 2019 Wang, Lin and Dang.

            This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

            Counts
            Figures: 12, Tables: 3, Equations: 33, References: 47, Pages: 16, Words: 9115
            Categories
            Neuroscience
            Original Research

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