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Title: Parallel simulation of spiking neural networks
Authors: Tang, Marcus Zi Yang
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Tang, M. Z. Y. (2021). Parallel simulation of spiking neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE20-0931
Abstract: Spiking neural networks transfer information through activation spikes that carry information through their weight and temporal delay. The behavior of a spiking neural network can be simulated through discrete event simulation, where neurons are framed as discrete logical processes. However, due to the large size of the network and the number of update events occurring, sequential discrete event simulation is unable to simulate a large population of spiking neurons effectively. Application of parallel discrete event simulation techniques to simulate spiking neural networks allows training using biologically plausible learning rules on a large-scale platform that can be potentially decentralized. In this report, a prototype for GPU-based discrete-event simulation of spiking neurons is explored, demonstrating convergence and unsupervised learning using GPU-based algorithms.
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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