guest ::
| Home > Workflow collections > Publication Charges > Passing the message: representation transfer in modular balanced networks > print |
| Home > Workflow collections > Publication Charges > Passing the message: representation transfer in modular balanced networks > print |
| 001 | 866432 | ||
| 005 | 20240313103111.0 | ||
| 024 | 7 | _ | |a 10.3389/fncom.2019.00079 |2 doi |
| 024 | 7 | _ | |a 2128/24196 |2 Handle |
| 024 | 7 | _ | |a altmetric:72299320 |2 altmetric |
| 024 | 7 | _ | |a pmid:31920605 |2 pmid |
| 024 | 7 | _ | |a WOS:000503494900001 |2 WOS |
| 037 | _ | _ | |a FZJ-2019-05574 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Zajzon, Barna |0 P:(DE-Juel1)171197 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Passing the message: representation transfer in modular balanced networks |
| 260 | _ | _ | |a Lausanne |c 2019 |b Frontiers Research Foundation |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1613576059_16659 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems' computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust, and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge toward which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks. |
| 536 | _ | _ | |a 574 - Theory, modelling and simulation (POF3-574) |0 G:(DE-HGF)POF3-574 |c POF3-574 |f POF III |x 0 |
| 536 | _ | _ | |a EUROSPIN - European Consortium on Synaptic Protein Networks in Neurological and Psychiatric Diseases (241498) |0 G:(EU-Grant)241498 |c 241498 |f FP7-HEALTH-2009-single-stage |x 1 |
| 536 | _ | _ | |a SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) |0 G:(DE-Juel1)HGF-SMHB-2013-2017 |c HGF-SMHB-2013-2017 |f SMHB |x 2 |
| 536 | _ | _ | |a Functional Neural Architectures (jinm60_20190501) |0 G:(DE-Juel1)jinm60_20190501 |c jinm60_20190501 |f Functional Neural Architectures |x 3 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Mahmoudian, Sepehr |0 P:(DE-Juel1)166250 |b 1 |
| 700 | 1 | _ | |a Morrison, Abigail |0 P:(DE-Juel1)151166 |b 2 |
| 700 | 1 | _ | |a Duarte, Renato |0 P:(DE-Juel1)165640 |b 3 |
| 773 | _ | _ | |a 10.3389/fncom.2019.00079 |g Vol. 13, p. 79 |0 PERI:(DE-600)2452964-3 |p 79 |t Frontiers in computational neuroscience |v 13 |y 2019 |x 1662-5188 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/866432/files/2019-0207098-3.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/866432/files/2019-0207098-3.pdf?subformat=pdfa |x pdfa |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/866432/files/fncom-13-00079.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/866432/files/fncom-13-00079.pdf?subformat=pdfa |x pdfa |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:866432 |p openaire |p open_access |p OpenAPC |p driver |p VDB |p ec_fundedresources |p openCost |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171197 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)151166 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)165640 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-574 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v Theory, modelling and simulation |x 0 |
| 913 | 2 | _ | |a DE-HGF |b Programmungebundene Forschung |l ohne Programm |1 G:(DE-HGF)POF4-890 |0 G:(DE-HGF)POF4-899 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-800 |4 G:(DE-HGF)POF |v ohne Topic |x 0 |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b FRONT COMPUT NEUROSC : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Blind peer review |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-6-20090406 |k INM-6 |l Computational and Systems Neuroscience |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)IAS-6-20130828 |k IAS-6 |l Theoretical Neuroscience |x 1 |
| 920 | 1 | _ | |0 I:(DE-82)080012_20140620 |k JARA-HPC |l JARA - HPC |x 2 |
| 980 | 1 | _ | |a APC |
| 980 | 1 | _ | |a FullTexts |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-6-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| 980 | _ | _ | |a I:(DE-82)080012_20140620 |
| 980 | _ | _ | |a APC |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)IAS-6-20130828 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|