A Quaternionic Rate-Based Synaptic Learning Rule Derived from Spike-Timing Dependent Plasticity

  • Guang Qiao
  • Hongyue Du
  • Yi ZengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10261)


Most of the differential Hebbian rules derived from Spike-Timing Dependent Plasticity (STDP) focus on the rates of change of post-synaptic activity that carries the information about the future and enables the neural network to predict. And the current model mainly consider three factors for the adjustment of synaptic weight, namely, the rate of pre- and post-synaptic activity and the rate of change of post-synaptic activity. We argue that the rate of change of pre-synaptic activity also plays an important role on the adjustment of synaptic weight. Hence, this paper proposes a quaternionic rate-based synaptic learning rule that depends on four elements, namely, the instantaneous firing rates of both pre- and post-synaptic neurons and their time derivatives.


Spike-Timing Dependent Plasticity Quaternionic rate-based synaptic learning rule Instantaneous firing rate 



This study was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060007), and Beijing Municipal Commission of Science and Technology (Z151100000915070, Z161100000216124). This research is conducted at Institute of Automation, Chinese Academy of Science.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Harbin University of Science and TechnologyHarbinChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiChina
  4. 4.University of Chinese Academy of SciencesBeijingChina

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