International Joint Conference on Neural Networks由国际神经网络学会(INNS)与IEEE计算智能学会合作举办,是神经网络及相关领域研究人员和其他专业人员的首次国际会议。该会议将邀请世界知名演讲者就神经网络理论和应用、计算神经科学、机器人学和分布式智能领域进行演讲。除了定期举行口头和海报介绍的技术会议外,会议计划还将包括特别会议、竞赛、辅导和有关当前感兴趣主题的讲习班。官网链接:


In this paper, we present MASTISK (MAchine-learning and Synaptic-plasticity Technology Integrated Simulation frameworK). MASTISK is an open-source versatile and flexible tool developed in MATLAB for design exploration of dedicated neuromorphic hardware using nanodevices and hybrid CMOS-nanodevice circuits. MASTISK has a hierarchical organization capturing details at the level of devices, circuits (i.e. neurons or activation functions, synapses or weights) and architectures (i.e. topology, learning-rules, algorithms). In the current version, MASTISK provides user-friendly interface for design and simulation of spiking neural networks (SNN) powered by spatio-temporal learning rules such as Spike-Timing Dependent Plasticity (STDP). Users may provide network definition as a simple input parameter file and the framework is capable of performing automated learning/inference simulations. Validation case-studies of the proposed open source simulator will be published in the proceedings of IJCNN 2018. The proposed framework offers new functionalities, compared to similar simulation tools in literature, such as: (i) arbitrary synaptic circuit modeling capability with both identical and non-identical stimuli, (ii) arbitrary spike modeling, and (iii) nanodevice based neuron emulation. The code of MASTISK is available on request at: IITD Research/MASTISK/wikis/home