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Causal inference and explaining away in a spiking network

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dc.contributor.author Moreno Bote, Rubén
dc.contributor.author Drugowitsch, Jan
dc.date.accessioned 2019-06-18T08:48:52Z
dc.date.available 2019-06-18T08:48:52Z
dc.date.issued 2015
dc.identifier.citation Moreno Bote R, Drugowitsch J. Causal inference and explaining away in a spiking network. Sci Rep. 2015 Dec 1;5:17531. DOI: 10.1038/srep17531
dc.identifier.issn 2045-2322
dc.identifier.uri http://hdl.handle.net/10230/41809
dc.description.abstract While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.
dc.description.sponsorship This work has been supported by the Ramón y Cajal Spanish Award RYC-2010-05952, the Marie Curie FP7-PEOPLE-2010-IRG grant PIRG08-GA-2010-276795, and the Spanish PSI2013-44811-P grant.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Nature Research
dc.relation.ispartof Scientific Reports. 2015 Dec 1;5:17531.
dc.rights © This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Causal inference and explaining away in a spiking network
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://dx.doi.org/10.1038/srep17531
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/PSI2013-44811-P
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/276795
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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