  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:
https://gitlab.com/NVM IITD Research/MASTISK/wikis/home
