Analyzing large-scale spiking neural data with HRLAnalysis™
The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in...
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| Format: | Online |
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Frontiers Media S.A.
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3942659/ |
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pubmed-3942659 |
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pubmed-39426592014-03-14 Analyzing large-scale spiking neural data with HRLAnalysis™ Thibeault, Corey M. O'Brien, Michael J. Srinivasa, Narayan Neuroscience The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in data can make analyzing the results of these simulations difficult. This is further compounded by the fact that many existing analysis packages were not developed with large spiking data sets in mind. Presented here is a software suite developed to not only process the increased amount of spike-train data in a reasonable amount of time, but also provide a user friendly Python interface. We describe the design considerations, implementation and features of the HRLAnalysis™ suite. In addition, performance benchmarks demonstrating the speedup of this design compared to a published Python implementation are also presented. The result is a high-performance analysis toolkit that is not only usable and readily extensible, but also straightforward to interface with existing Python modules. Frontiers Media S.A. 2014-03-05 /pmc/articles/PMC3942659/ /pubmed/24634655 http://dx.doi.org/10.3389/fninf.2014.00017 Text en Copyright © 2014 HRL Laboratories LLC. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Thibeault, Corey M. O'Brien, Michael J. Srinivasa, Narayan |
| spellingShingle |
Thibeault, Corey M. O'Brien, Michael J. Srinivasa, Narayan Analyzing large-scale spiking neural data with HRLAnalysis™ |
| author_facet |
Thibeault, Corey M. O'Brien, Michael J. Srinivasa, Narayan |
| author_sort |
Thibeault, Corey M. |
| title |
Analyzing large-scale spiking neural data with HRLAnalysis™ |
| title_short |
Analyzing large-scale spiking neural data with HRLAnalysis™ |
| title_full |
Analyzing large-scale spiking neural data with HRLAnalysis™ |
| title_fullStr |
Analyzing large-scale spiking neural data with HRLAnalysis™ |
| title_full_unstemmed |
Analyzing large-scale spiking neural data with HRLAnalysis™ |
| title_sort |
analyzing large-scale spiking neural data with hrlanalysis™ |
| description |
The additional capabilities provided by high-performance neural simulation environments and modern computing hardware has allowed for the modeling of increasingly larger spiking neural networks. This is important for exploring more anatomically detailed networks but the corresponding accumulation in data can make analyzing the results of these simulations difficult. This is further compounded by the fact that many existing analysis packages were not developed with large spiking data sets in mind. Presented here is a software suite developed to not only process the increased amount of spike-train data in a reasonable amount of time, but also provide a user friendly Python interface. We describe the design considerations, implementation and features of the HRLAnalysis™ suite. In addition, performance benchmarks demonstrating the speedup of this design compared to a published Python implementation are also presented. The result is a high-performance analysis toolkit that is not only usable and readily extensible, but also straightforward to interface with existing Python modules. |
| publisher |
Frontiers Media S.A. |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3942659/ |
| _version_ |
1612064423262814208 |