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Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise

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contributor authorSafaryan, Karen
contributor authorMaex, Reinoud
contributor authorDavey, Neil
contributor authorAdams, Roderick
contributor authorSteuber, Volker
date accessioned2017-05-17T15:11:20Z
date available2017-05-17T15:11:20Z
date issued2017-04-20
identifier citationSafaryan , K , Maex , R , Davey , N , Adams , R & Steuber , V 2017 , ' Nonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noise ' Scientific Reports . DOI: 10.1038/srep46550en
identifier issn2045-2322
identifier otherPURE: 10871644
identifier otherPURE UUID: b25f3335-5672-4f42-b812-b0986b52e7d1
identifier urihttp://hdl.handle.net/2299/18197
description abstractMany forms of synaptic plasticity require the local production of volatile or rapidly diffusing substances such as nitric oxide. The nonspecific plasticity these neuromodulators may induce at neighboring non-active synapses is thought to be detrimental for the specificity of memory storage. We show here that memory retrieval may benefit from this non-specific plasticity when the applied sparse binary input patterns are degraded by local noise. Simulations of a biophysically realistic model of a cerebellar Purkinje cell in a pattern recognition task show that, in the absence of noise, leakage of plasticity to adjacent synapses degrades the recognition of sparse static patterns. However, above a local noise level of 20 %, the model with nonspecific plasticity outperforms the standard, specific model. The gain in performance is greatest when the spatial distribution of noise in the input matches the range of diffusion-induced plasticity. Hence non-specific plasticity may offer a benefit in noisy environments or when the pressure to generalize is strong.en
language isoeng
relation ispartofScientific Reportsen
rightsen
titleNonspecific synaptic plasticity improves the recognition of sparse patterns degraded by local noiseen
typeArticleen
contributor institutionSchool of Computer Scienceen
contributor institutionCentre for Computer Science and Informatics Researchen
identifier doihttp://dx.doi.org/10.1038/srep46550
description statusPeer revieweden


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