Title:
Accelerating biophysical neural network simulation with region of interest based approximation

dc.contributor.advisor Mukhopadhyay, Saibal
dc.contributor.author Long, Yun
dc.contributor.committeeMember Raychowdhury, Arijit
dc.contributor.committeeMember Khan, Asif Islam
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2020-09-08T12:38:45Z
dc.date.available 2020-09-08T12:38:45Z
dc.date.created 2019-05
dc.date.submitted May 2019
dc.date.updated 2020-09-08T12:38:45Z
dc.description.abstract Modeling the dynamics of biophysical neural network (BNN) is essential to understand brain operation and design cognitive systems. Large-scale and biophysically plausible BNN modeling requires solving multiple-terms, coupled and non-linear differential equations, making simulation computationally complex and memory intensive. In this work, an adaptive simulation methodology is presented in which neurons in the region of interest (ROI) follow high biological accurate models while the other neurons follow computation friendly models. To enable ROI based approximation, we propose a generic template based computing algorithm which unifies the data structure and computing flow for various neuron models. We implement the algorithms on CPU, GPU and embedded platforms, showing 11x speedup with insignificant loss of biological details in the region of interest.
dc.description.degree M.S.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/63494
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Spiking neural network
dc.subject Machine learning
dc.subject ROI based approximation
dc.subject GPU computing
dc.title Accelerating biophysical neural network simulation with region of interest based approximation
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Mukhopadhyay, Saibal
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 62df0580-589a-4599-98af-88783123945a
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Masters
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