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dc.contributor.authorHeeger, David J.-
dc.contributor.authorZemlianova, Klavdia O.-
dc.date.accessioned2020-03-22T18:26:44Z-
dc.date.available2020-03-22T18:26:44Z-
dc.date.issued2020-03-22-
dc.identifier.citationHeeger DJ, Zemlianova KO, "A recurrent circuit implements normalization, simulating the dynamics of V1 activity", PNAS, 2020en
dc.identifier.urihttp://hdl.handle.net/2451/61045-
dc.descriptionMatlab code in this directory computes the results published in: Heeger DJ, Zemlianova KO, A recurrent circuit implements normalization, simulating the dynamics of V1 activity, Proceedings of the National Academy of Sciences, 2020. Preprint: Heeger DJ, Zemlianova KO, Dynamic Normalization, bioRxiv 10.1101/2020.03.22.002634, 2020.en
dc.description.abstractThe normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model’s defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. In spite of the success of the normalization model, there are 3 unresolved issues. 1) Experimental evidence suggests that normalization in V1 operates via recurrent amplification, i.e., amplifying weak inputs more than strong inputs. It is unknown how normalization arises from recurrent amplification. 2) Experiments have demonstrated that normalization is weighted such each weight specifies how one neuron contributes to another’s normalization pool. It is unknown how weighted normalization arises from a recurrent circuit. 3) Neural activity in V1 exhibits complex dynamics, including gamma oscillations, linked to normalization. It is unknown how these dynamics emerge from normalization. Here, a new family of recurrent circuit models is reported, each of which comprises coupled neural integrators to implement normalization via recurrent amplification with arbitrary normalization weights, some of which can recapitulate key experimental observations of the dynamics of neural activity in V1.en
dc.publisherNational Academy of Sciencesen
dc.titleSupplemental Material for "Dynamic Normalization"en
dc.typeSoftwareen
Appears in Collections:Heeger Lab Collection

Files in This Item:
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Heeger Zemlianova bioRxiv May 30 2020.pdfPreprint6.36 MBAdobe PDFView/Open
ORGaNIC Normalization Matlab Code 3-22-2020.zipMatlab code zip archive88.14 MBzip archiveView/Open
README.mReadme text file3.23 kBText readable Matlab fileView/Open


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