Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on biomedical circuits and systems Jg. 13; H. 3; S. 579 - 591 |
|---|---|
| Hauptverfasser: | , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
IEEE
01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1932-4545, 1940-9990, 1940-9990 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions, such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the second generation SpiNNaker system is designed to overcome this problem. Low-power advanced RISC machine (ARM) processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations, the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local static random access memory (SRAM), leads to 62% energy reduction compared to the case without accelerators and the use of external dynamic random access memory (DRAM). The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this paper paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms. |
|---|---|
| AbstractList | Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions, such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the second generation SpiNNaker system is designed to overcome this problem. Low-power advanced RISC machine (ARM) processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations, the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local static random access memory (SRAM), leads to 62% energy reduction compared to the case without accelerators and the use of external dynamic random access memory (DRAM). The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this paper paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms. Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions, such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the second generation SpiNNaker system is designed to overcome this problem. Low-power advanced RISC machine (ARM) processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations, the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local static random access memory (SRAM), leads to 62% energy reduction compared to the case without accelerators and the use of external dynamic random access memory (DRAM). The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this paper paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms.Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon substrate, since brain-inspired algorithms often make extensive use of complex functions, such as random number generators, that are expensive to compute on standard general purpose hardware. The prototype chip of the second generation SpiNNaker system is designed to overcome this problem. Low-power advanced RISC machine (ARM) processors equipped with a random number generator and an exponential function accelerator enable the efficient execution of brain-inspired algorithms. We implement the recently introduced reward-based synaptic sampling model that employs structural plasticity to learn a function or task. The numerical simulation of the model requires to update the synapse variables in each time step including an explorative random term. To the best of our knowledge, this is the most complex synapse model implemented so far on the SpiNNaker system. By making efficient use of the hardware accelerators and numerical optimizations, the computation time of one plasticity update is reduced by a factor of 2. This, combined with fitting the model into to the local static random access memory (SRAM), leads to 62% energy reduction compared to the case without accelerators and the use of external dynamic random access memory (DRAM). The model implementation is integrated into the SpiNNaker software framework allowing for scalability onto larger systems. The hardware-software system presented in this paper paves the way for power-efficient mobile and biomedical applications with biologically plausible brain-inspired algorithms. |
| Author | Maass, Wolfgang Hoppner, Sebastian Furber, Steve Legenstein, Robert Mayr, Christian Kappel, David Partzsch, Johannes Vogginger, Bernhard Yan, Yexin Neumarker, Felix |
| Author_xml | – sequence: 1 givenname: Yexin orcidid: 0000-0002-4267-7015 surname: Yan fullname: Yan, Yexin email: yexin.yan@tu-dresden.de organization: Technische Universität Dresden, Dresden, Germany – sequence: 2 givenname: David surname: Kappel fullname: Kappel, David email: david.kappel@phys.uni-goettingen.de organization: Technische Universität Dresden, Dresden, Germany – sequence: 3 givenname: Felix surname: Neumarker fullname: Neumarker, Felix email: felix.neumaerker@tu-dresden.de organization: Technische Universität Dresden, Dresden, Germany – sequence: 4 givenname: Johannes orcidid: 0000-0002-6286-5064 surname: Partzsch fullname: Partzsch, Johannes email: johannes.partzsch@tu-dresden.de organization: Technische Universität Dresden, Dresden, Germany – sequence: 5 givenname: Bernhard orcidid: 0000-0001-9042-5405 surname: Vogginger fullname: Vogginger, Bernhard email: Bernhard.Vogginger@tu-dresden.de organization: Technische Universität Dresden, Dresden, Germany – sequence: 6 givenname: Sebastian orcidid: 0000-0002-9938-2736 surname: Hoppner fullname: Hoppner, Sebastian email: sebastian.hoeppner@tu-dresden.de organization: Technische Universität Dresden, Dresden, Germany – sequence: 7 givenname: Steve orcidid: 0000-0002-6524-3367 surname: Furber fullname: Furber, Steve email: steve.furber@manchester.ac.uk organization: School of Computer Science, University of Manchester, Manchester, U.K – sequence: 8 givenname: Wolfgang orcidid: 0000-0002-1178-087X surname: Maass fullname: Maass, Wolfgang email: maass@igi.tugraz.at organization: Institute for Theoretical Computer Science, Technische Universität Graz, Graz, Austria – sequence: 9 givenname: Robert orcidid: 0000-0002-8724-5507 surname: Legenstein fullname: Legenstein, Robert email: robert.legenstein@igi.tugraz.at organization: Institute for Theoretical Computer Science, Technische Universität Graz, Graz, Austria – sequence: 10 givenname: Christian surname: Mayr fullname: Mayr, Christian email: christian.mayr@tu-dresden.de organization: Technische Universität Dresden, Dresden, Germany |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30932847$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kc1KJDEUhYMo48_MCyhIgRs31eanklSW2jg6ICq2sw63U7egtLrSJimk337SduvCxZBFAvm-y-WcQ7I7-AEJOWZ0whg1F89X08vZhFNmJtxQVVG2Qw6YqWhpjKG767fgZSUruU8OY3yhVCpu-A-yL2j-qSt9QG6u27ZzHQ6peMJ3CE15BRGbYpbC6NIYoC8ee4gpM2lV-KGAYrbs7u_hFUPBi8fgk0-rJf4key30EX9t7yPy9_f18_S2vHu4-TO9vCudMDKVzsxryTRrkCEwp4yrK1erxig1bykIymvNTCuVNFQaDRJRK9GAFgBKAoojcr6Zuwz-bcSY7KKLDvseBvRjtDzHofMxMqNn39AXP4Yhb5cpwWpVaakzdbqlxvkCG7sM3QLCyn5GlIF6A7jgYwzY2hwFpM4PKUDXW0btug370YZdt2G3bWSVf1M_p_9XOtlIHSJ-CbXSshJS_APJBpL3 |
| CODEN | ITBCCW |
| CitedBy_id | crossref_primary_10_1049_cds2_12160 crossref_primary_10_3389_fnins_2021_611300 crossref_primary_10_3390_electronics9101599 crossref_primary_10_3389_fnins_2022_1018006 crossref_primary_10_1016_j_neuroscience_2021_10_001 crossref_primary_10_1016_j_sse_2024_109033 crossref_primary_10_3389_fnins_2025_1511972 crossref_primary_10_1038_s41586_024_08253_8 crossref_primary_10_1002_advs_202402375 crossref_primary_10_1109_JETCAS_2020_3040248 crossref_primary_10_1016_j_csbj_2024_10_040 crossref_primary_10_1109_TNNLS_2025_3547774 crossref_primary_10_1016_j_matt_2023_03_016 crossref_primary_10_1016_j_neunet_2020_09_024 crossref_primary_10_3389_fnins_2021_709053 crossref_primary_10_1038_s41467_023_37097_5 crossref_primary_10_1109_TBCAS_2021_3089132 |
| Cites_doi | 10.1214/aoms/1177706645 10.3389/fnins.2015.00002 10.1371/journal.pcbi.1003846 10.3389/fnins.2018.00213 10.1038/nrn3061 10.1142/S0129183191001037 10.1371/journal.pbio.1002572 10.3389/fnins.2015.00227 10.1109/TBCAS.2017.2759700 10.3389/fnins.2018.00291 10.1016/j.neuron.2007.04.030 10.1109/ISCAS.2017.8050528 10.1109/JPROC.2014.2304638 10.1016/j.mejo.2008.12.005 10.3389/fnins.2018.00105 10.3389/fnins.2018.00840 10.1109/TBCAS.2014.2379294 10.1007/s13295-016-0026-2 10.3389/fnins.2018.00816 10.1523/JNEUROSCI.0603-08.2008 10.1109/MM.2018.112130359 10.1109/JPROC.2014.2313565 10.1109/JSSC.2007.914337 10.1523/ENEURO.0301-17.2018 10.3389/fnins.2018.00941 10.1016/j.parco.2015.01.002 10.3389/fnins.2018.00891 10.1109/IJCNN.2017.7966125 10.1038/nrn2258 10.1109/TNNLS.2016.2572164 10.1109/EBCCSP.2015.7300664 10.1109/JPROC.2014.2314454 10.1523/JNEUROSCI.6130-10.2011 10.1017/CBO9781107447615 10.1145/3061639.3062188 10.1145/945511.945517 10.1016/j.neuron.2016.10.046 10.3389/fnins.2015.00010 10.1109/TCSII.2013.2278123 10.1109/IEDM.2015.7409623 10.1371/journal.pcbi.1004485 10.3389/fnins.2013.00118 10.18637/jss.v008.i14 10.3389/fnins.2015.00141 10.1109/ISCAS.2017.8050530 10.1038/nature04783 10.1109/JSSC.2013.2259038 10.1126/science.1254642 10.1109/TCSII.2018.2824827 10.3389/fnins.2018.00434 10.1109/NORCHIP.2016.7792875 10.1109/IJCNN.2013.6706988 10.1145/2897937.2897986 10.1109/JPROC.2014.2310593 10.1016/j.neuron.2005.01.003 10.1098/rspb.2011.2629 10.1109/TBCAS.2016.2579164 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 7SP 7TB 8FD FR3 L7M P64 7X8 |
| DOI | 10.1109/TBCAS.2019.2906401 |
| DatabaseName | IEEE Xplore (IEEE) IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Biotechnology Research Abstracts Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Advanced Technologies Database with Aerospace Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Biotechnology Research Abstracts Technology Research Database Mechanical & Transportation Engineering Abstracts Electronics & Communications Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Biotechnology Research Abstracts MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1940-9990 |
| EndPage | 591 |
| ExternalDocumentID | 30932847 10_1109_TBCAS_2019_2906401 8675435 |
| Genre | orig-research Research Support, Non-U.S. Gov't Journal Article |
| GrantInformation_xml | – fundername: EU's Horizon 2020 research and innovation programme grantid: 720270; 785907 – fundername: Austrian Science Fund grantid: I 3251-N33 funderid: 10.13039/501100002428 – fundername: European Union Seventh Framework Programme grantid: 604102 – fundername: H2020-FETPROACT project grantid: 732266 – fundername: Austrian Science Fund FWF grantid: I 3251 |
| GroupedDBID | --- 0R~ 29I 4.4 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACIWK ACPRK AENEX AETIX AFRAH AGQYO AGSQL AHBIQ AKJIK AKQYR ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD ESBDL F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS AAYXX CITATION CGR CUY CVF ECM EIF NPM RIG 7QO 7SP 7TB 8FD FR3 L7M P64 7X8 |
| ID | FETCH-LOGICAL-c395t-c9b85171de1ea1c69c84c86d966bf0a3028719f56590597a5ee763da73aa65ae3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 25 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000469839700009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1932-4545 1940-9990 |
| IngestDate | Sun Sep 28 14:08:43 EDT 2025 Mon Jun 30 08:39:50 EDT 2025 Mon Jul 21 06:02:39 EDT 2025 Sat Nov 29 05:32:51 EST 2025 Tue Nov 18 22:16:38 EST 2025 Wed Aug 27 02:46:42 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0/legalcode |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c395t-c9b85171de1ea1c69c84c86d966bf0a3028719f56590597a5ee763da73aa65ae3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-6286-5064 0000-0001-9042-5405 0000-0002-9938-2736 0000-0002-4267-7015 0000-0002-8724-5507 0000-0002-6524-3367 0000-0002-1178-087X |
| OpenAccessLink | https://ieeexplore.ieee.org/document/8675435 |
| PMID | 30932847 |
| PQID | 2231864757 |
| PQPubID | 85510 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_2201717195 crossref_primary_10_1109_TBCAS_2019_2906401 proquest_journals_2231864757 crossref_citationtrail_10_1109_TBCAS_2019_2906401 ieee_primary_8675435 pubmed_primary_30932847 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-06-01 |
| PublicationDateYYYYMMDD | 2019-06-01 |
| PublicationDate_xml | – month: 06 year: 2019 text: 2019-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: New York |
| PublicationTitle | IEEE transactions on biomedical circuits and systems |
| PublicationTitleAbbrev | TBCAS |
| PublicationTitleAlternate | IEEE Trans Biomed Circuits Syst |
| PublicationYear | 2019 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref57 ref12 ref59 ref58 ref14 ref53 ref52 ref55 ref11 ref54 ref10 ref17 ref16 ref19 ref18 frenkel (ref64) 2019; 13 ref51 ref50 höppner (ref38) 2018 ref46 ref45 ref48 höppner (ref33) 0 ref41 ref44 ref43 henker (ref56) 0 ref8 ref7 han (ref49) 0 ref9 ref4 ref3 ref6 hopkins (ref47) 2014 ref5 ref40 ref35 höppner (ref32) 0 ref34 ref37 ref36 ref31 ref30 ref2 ref1 ref39 bellec (ref15) 0 ref24 ref67 ref23 ref26 ref25 ref20 ref63 ref66 ref22 noack (ref42) 0 ref65 ref21 ref28 ref27 ref29 kappel (ref13) 0 ref60 ref62 ref61 |
| References_xml | – start-page: 219 year: 0 ident: ref42 article-title: Biology-derived synaptic dynamics and optimized system architecture for neuromorphic hardware publication-title: Proc 14th Int Conf Mixed Des Integr Circuits Syst – ident: ref44 doi: 10.1214/aoms/1177706645 – ident: ref24 doi: 10.3389/fnins.2015.00002 – ident: ref9 doi: 10.1371/journal.pcbi.1003846 – ident: ref60 doi: 10.3389/fnins.2018.00213 – ident: ref10 doi: 10.1038/nrn3061 – ident: ref46 doi: 10.1142/S0129183191001037 – ident: ref5 doi: 10.1371/journal.pbio.1002572 – start-page: 1135 year: 0 ident: ref49 article-title: Learning both weights and connections for efficient neural networks publication-title: Proc 28th Int Conf Neural Inf Process Syst – ident: ref18 doi: 10.3389/fnins.2015.00227 – ident: ref59 doi: 10.1109/TBCAS.2017.2759700 – ident: ref52 doi: 10.3389/fnins.2018.00291 – ident: ref6 doi: 10.1016/j.neuron.2007.04.030 – year: 2018 ident: ref38 article-title: Method for generating true random numbers on a multiprocessor system and the same – ident: ref26 doi: 10.1109/ISCAS.2017.8050528 – ident: ref27 doi: 10.1109/JPROC.2014.2304638 – ident: ref37 doi: 10.1016/j.mejo.2008.12.005 – ident: ref48 doi: 10.3389/fnins.2018.00105 – ident: ref41 doi: 10.3389/fnins.2018.00840 – start-page: 1 year: 0 ident: ref32 article-title: SpiNNaker 2-Towards extremely efficient digital neuromorphics and multi-scale brain emulation publication-title: Proc 5th Neuro-Inspired Comput Elements Workshop – ident: ref65 doi: 10.1109/TBCAS.2014.2379294 – ident: ref4 doi: 10.1007/s13295-016-0026-2 – ident: ref50 doi: 10.3389/fnins.2018.00816 – ident: ref7 doi: 10.1523/JNEUROSCI.0603-08.2008 – ident: ref61 doi: 10.1109/MM.2018.112130359 – ident: ref58 doi: 10.1109/JPROC.2014.2313565 – ident: ref55 doi: 10.1109/JSSC.2007.914337 – ident: ref14 doi: 10.1523/ENEURO.0301-17.2018 – ident: ref23 doi: 10.3389/fnins.2018.00941 – ident: ref34 doi: 10.1016/j.parco.2015.01.002 – ident: ref66 doi: 10.3389/fnins.2018.00891 – ident: ref20 doi: 10.1109/IJCNN.2017.7966125 – ident: ref1 doi: 10.1038/nrn2258 – start-page: 1 year: 0 ident: ref15 article-title: Deep rewiring: Training very sparse deep networks publication-title: Proc Int Conf Learn Representations – ident: ref54 doi: 10.1109/TNNLS.2016.2572164 – ident: ref43 doi: 10.1109/EBCCSP.2015.7300664 – ident: ref67 doi: 10.1109/JPROC.2014.2314454 – start-page: 1 year: 0 ident: ref33 article-title: Dynamic voltage and frequency scaling for neuromorphic many-core systems publication-title: Proc IEEE Int Symp Circuits Syst – ident: ref8 doi: 10.1523/JNEUROSCI.6130-10.2011 – ident: ref40 doi: 10.1017/CBO9781107447615 – ident: ref29 doi: 10.1145/3061639.3062188 – ident: ref45 doi: 10.1145/945511.945517 – year: 2014 ident: ref47 – ident: ref30 doi: 10.1016/j.neuron.2016.10.046 – ident: ref17 doi: 10.3389/fnins.2015.00010 – ident: ref36 doi: 10.1109/TCSII.2013.2278123 – volume: 13 start-page: 145 year: 2019 ident: ref64 article-title: A 0.086-mm$^2$ 12.7-pj/sop 64k-synapse 256-neuron online-learning digital spiking neuromorphic processor in 28-nm CMOS publication-title: IEEE Trans Biomed Circuits Syst – ident: ref16 doi: 10.1109/IEDM.2015.7409623 – start-page: 16 year: 0 ident: ref56 article-title: Active pixel sensor arrays in 90/65nm CMOS-technologies with vertically stacked photodiodes publication-title: Proc IEEE Int Image Sensor Workshop – ident: ref12 doi: 10.1371/journal.pcbi.1004485 – ident: ref53 doi: 10.3389/fnins.2013.00118 – ident: ref35 doi: 10.18637/jss.v008.i14 – ident: ref63 doi: 10.3389/fnins.2015.00141 – ident: ref21 doi: 10.1109/ISCAS.2017.8050530 – ident: ref39 doi: 10.1038/nature04783 – ident: ref31 doi: 10.1109/JSSC.2013.2259038 – ident: ref22 doi: 10.1126/science.1254642 – ident: ref19 doi: 10.1109/TCSII.2018.2824827 – ident: ref57 doi: 10.3389/fnins.2018.00434 – ident: ref25 doi: 10.1109/NORCHIP.2016.7792875 – ident: ref51 doi: 10.1109/IJCNN.2013.6706988 – ident: ref28 doi: 10.1145/2897937.2897986 – start-page: 370 year: 0 ident: ref13 article-title: Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring publication-title: Proc Adv Neural Inf Process Syst – ident: ref11 doi: 10.1109/JPROC.2014.2310593 – ident: ref3 doi: 10.1016/j.neuron.2005.01.003 – ident: ref2 doi: 10.1098/rspb.2011.2629 – ident: ref62 doi: 10.1109/TBCAS.2016.2579164 |
| SSID | ssj0056292 |
| Score | 2.3843753 |
| Snippet | Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the... |
| SourceID | proquest pubmed crossref ieee |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 579 |
| SubjectTerms | Accelerators Algorithms Bayesian reinforcement learning Biomedical materials Brain Brain - physiology Brain modeling Clocks Computational modeling Computer programs Computer simulation Dynamic random access memory exponential function accelerator Exponential functions Generators Hardware Humans Learning Machine Learning Mathematical models Models, Neurological Nervous system Neural Networks, Computer neuromorphic computing Plastic properties Plasticity Prototypes Random access memory random number generator Random numbers Reinforcement Silicon substrates Software SpiNNaker chip Static random access memory structural plasticity Synapses - physiology Synaptic plasticity synaptic sampling Task complexity |
| Title | Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype |
| URI | https://ieeexplore.ieee.org/document/8675435 https://www.ncbi.nlm.nih.gov/pubmed/30932847 https://www.proquest.com/docview/2231864757 https://www.proquest.com/docview/2201717195 |
| Volume | 13 |
| WOSCitedRecordID | wos000469839700009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1940-9990 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0056292 issn: 1932-4545 databaseCode: RIE dateStart: 20070101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT-QwDLYAcYADu8tzWBYFiRsU2knbNEdAsJxGIwakuVVp4koI1I6GAYl_j50-tAcWiVulOmkaJ_HnxPkMcCzjUhbD0AZYOhPEblgGhct0EJtC21LZxKHzySbUaJRNp3q8BKf9XRhE9MFneMaP_izf1faVt8rOM0K3ZN6XYVmptLmr1a26ZMZ9AmTGI8zjnXQXZEJ9fn95dTHhKC59xuTmcZsApjNCPqvK_wGmNzQ3P77XxJ-w0QJKcdGMgF-whNUmrP9DM7gFf689TwQVFHfoo2QvyXY5MfHcscy7IcYEojm-evEu6koYMZk9jkbmCediKMbzelHzVu02PNxc31_dBm0ChcBKnSwCqwsCVCpyGKGJbKptFtssdeTiFGVoZMjuki4J02lCWcokiLTcOKOkMWliUO7ASlVXuAfCapIjGSldGDuJGdWo0ojghHJp4coBRF2P5rZlF-ckF8-59zJCnXst5KyFvNXCAE76MrOGW-NL6S3u7l6y7ekBHHSKy9vp95IT5omyNFaJGsBR_5omDp-GmArrV5ZhqiD6f6pit1F4XzcfD7Pd3v_8m79hjVvWRIwdwAppC__Aqn1bPL7MD2l0TrNDPzo_ANU-30g |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VFgk40EJ5LLTFSL1B2mTtxPGxrfpAlGjVXaTeIseeSBUoqbZbJP49M85DHACJW6SMHcdje76xx98A7EtVy2oauwhrbyPlp3VU-dxEylbG1dqlHn1INqGLIr--NrM1-DjehUHEEHyGB_wYzvJ96-55q-wwJ3RL5v0BbKRKTePuttaw7pIhDymQGZEwk3c6XJGJzeHi-ORoznFc5oDpzVWfAmYwQyGvyt8hZjA1Z5v_18gteNpDSnHUjYFnsIbNc3jyG9HgNpyfBqYIKiiuMMTJHpP18mIe2GOZeUPMCEZzhPXqp2gbYcX89qYo7DdciqmYLdtVy5u1L-Dr2eni5CLqUyhETpp0FTlTEaTSiccEbeIy43Ll8syTk1PVsZUxO0ymJlRnCGdpmyLSguOtltZmqUX5EtabtsHXIJwhOZKR0sfKS8ypRp0lBCi0zypfTyAZerR0Pb84p7n4XgY_IzZl0ELJWih7LUzgw1jmtmPX-Kf0Nnf3KNn39AR2BsWV_QS8Kwn1JHmmdKon8H58TVOHz0Nsg-09yzBZEP0_VfGqU_hYNx8Qs-V-8-dvvoNHF4svl-Xlp-LzW3jMrezix3ZgnTSHu_DQ_Vjd3C33whj9BVgQ4ac |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Efficient+Reward-Based+Structural+Plasticity+on+a+SpiNNaker+2+Prototype&rft.jtitle=IEEE+transactions+on+biomedical+circuits+and+systems&rft.au=Yan%2C+Yexin&rft.au=Kappel%2C+David&rft.au=Neumarker%2C+Felix&rft.au=Partzsch%2C+Johannes&rft.date=2019-06-01&rft.pub=IEEE&rft.issn=1932-4545&rft.volume=13&rft.issue=3&rft.spage=579&rft.epage=591&rft_id=info:doi/10.1109%2FTBCAS.2019.2906401&rft.externalDocID=8675435 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-4545&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-4545&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-4545&client=summon |