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Date of Award

Spring 5-2018

Author's School

School of Engineering & Applied Science

Author's Department

Computer Science & Engineering

Degree Name

Master of Science (MS)

Degree Type

Thesis

Abstract

A neural network simulator for Spiking Neural Network (SNN) is a useful research tool to model brain functions with a computer. With this tool, different parameters can be explored easily compared to using a real brain. For several decades, researchers have developed many software packages and simulators to accelerate research in computational neuroscience. However, despite their advantages, different neural simulators possess different limitations, such as flexibility of choosing different neuron models and scalability of simulators for large numbers of neurons. This paper demonstrates an efficient and scalable spiking neural simulator that is based on growth transform neurons and runs on a single machine. The growth transform neuron model’s update is based on matrix-vector multiplication, which is optimized using external libraries named BLAS and sparseBLAS. Using sparseBLAS, the scalability of the simulator was optimized with sparse representation of matrix. The optimized tool can simulate up to 1 million neurons and is flexible with neuron model changes behind the simulator. Furthermore, with a simple graphical user interface, a researcher can easily design a variety of network topology with different parameters. He/she can visualize a coupling matrix, simulate a designed network and study the spike train with spike raster plot. This simulator will be made open source so that researchers can benefit from this for large-scale simulations.

Language

English (en)

Chair

Roch Guerin

Committee Members

Shantanu Chakrabartty Xuan Zhang Brian Kocoloski

Comments

Permanent URL: https://doi.org/10.7936/K7CZ36KJ

Available for download on Sunday, April 26, 2020

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