SPAYK: An environment for spiking neural network simulation

被引:2
作者
Gelen, Aykut Gorkem [1 ]
Atasoy, Ayten [2 ]
机构
[1] Erzincan Binali Yildirim Univ, Dept Elect & Elect Engn, Erzincan, Turkiye
[2] Karadeniz Tech Univ, Dept Elect & Elect Engn, Trabzon, Turkiye
关键词
Spiking neural network; STDP based learning; supervised classification; unsupervised pattern recognition;
D O I
10.55730/1300-0632.3995
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In research areas such as mobile robotics and computer vision, energy and computational efficiency have become critical. This has greatly increased interest in high-efficiency neuromorphic hardware and spiking neural networks. Because neuromorphic hardware is not yet widely available, spiking neural network studies are conducted by simulations. There are numerous simulators available today, each designed for a specific purpose. In this paper, a novel and open -source package (SPAYK) for simulating spiking neural networks is presented. SPAYK has been proposed to speed up spiking neural network research. In the majority of simulators, networks are expressed with differential equations and require advanced neuroscience knowledge since such simulators are generally designed for brain and neuroscience research. SPAYK, on the other hand, is specifically designed as a framework to easily design spiking neural networks for practical problems. SPAYK is an easy-to-use Python package. There are three fundamental classes in the core: the model class for creating neuron groups, the organization class for simulating tissues, and the learning class for synaptic plasticity. While developing and testing the SPAYK environment, various experiments were carried out. This study includes three of these experiments. In the first experiment, we investigated the behavior of a group of Izhikevich neurons for visual stimuli. Also, a single Izhikevich neuron has been trained to respond to a particular label in a supervised manner with synaptic plasticity. In the second experiment, a well-known experiment was repeated to validate SPAYK. In this experiment, a neuron trained by synaptic plasticity can recognize repetitive patterns in a spike train. In the third experiment, a similar neuron was simulated with stimuli with multiple labels adapted from the MNIST dataset. It has been shown that the neuron can classify a particular label by synaptic plasticity. All these experiments and the SPAYK environment are presented as open-source tools.
引用
收藏
页码:462 / 480
页数:20
相关论文
共 29 条
  • [1] Nengo: a Python']Python tool for building large-scale functional brain models
    Bekolay, Trevor
    Bergstra, James
    Hunsberger, Eric
    DeWolf, Travis
    Stewart, Terrence C.
    Rasmussen, Daniel
    Choo, Xuan
    Voelker, Aaron Russell
    Eliasmith, Chris
    [J]. FRONTIERS IN NEUROINFORMATICS, 2014, 7
  • [2] Spiking neural network-based target tracking control for autonomous mobile robots
    Cao, Zhiqiang
    Cheng, Long
    Zhou, Chao
    Gu, Nong
    Wang, Xu
    Tan, Min
    [J]. NEURAL COMPUTING & APPLICATIONS, 2015, 26 (08) : 1839 - 1847
  • [3] Cheng X, 2020, PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1519
  • [4] Choudhary T, 2018, INT CONF INTEL INFOR, P1
  • [5] Eliasmith C., 2002, NEURAL ENG COMPUTATI
  • [6] Fang WAC, SPIKINGJELLY
  • [7] Gerstner W, 2014, NEURONAL DYNAMICS: FROM SINGLE NEURONS TO NETWORKS AND MODELS OF COGNITION, P1, DOI 10.1017/CBO9781107447615
  • [8] Gerstner W., 2008, Scholarpedia, V3, P1343, DOI [DOI 10.4249/SCHOLARPEDIA.1343, 10.4249/scholarpedia.1343]
  • [9] Gewaltig M.-O., 2007, SCHOLARPEDIA, V2, P1430, DOI DOI 10.4249/SCHOLARPEDIA.1430
  • [10] Goodman Dan, 2008, Front Neuroinform, V2, P5, DOI 10.3389/neuro.11.005.2008