Digital implementation of a virtual insect trained by spike-timing dependent plasticity

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• 作者：P. Mazumder^a ; ^{pinakimazum@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; D. Hu^a ; ^{hudi@umich.edu" class="auth_mail" title="E-mail the corresponding author} ; I. Ebong^a ; ^{idong@eecs.umich.edu" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; X. Zhang^b ; ^{xz70@duke.edu" class="auth_mail" title="E-mail the corresponding author} ; Z. Xu^b ; ^{dec.ziyer@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; S. Ferrari^b ; ^{sferrari@duke.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Spike timing dependent plasticity ; Neural network
• 刊名：Integration, the VLSI Journal
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：54
• 期：Complete
• 页码：109-117
• 全文大小：2058 K

文摘

Neural network approach to processing have been shown successful and efficient in numerous real world applications. The most successful of this approach are implemented in software but in order to achieve real-time processing similar to that of biological neural networks, hardware implementations of these networks need to be continually improved. This work presents a spiking neural network (SNN) implemented in digital CMOS. The SNN is constructed based on an indirect training algorithm that utilizes spike-timing dependent plasticity (STDP). The SNN is validated by using its outputs to control the motion of a virtual insect. The indirect training algorithm is used to train the SNN to navigate through a terrain with obstacles. The indirect approach is more appropriate for nanoscale CMOS implementation synaptic training since it is getting more difficult to perfectly control matching in CMOS circuits.