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- W2768034240 abstract "The attractor neural network scenario is a popular scenario for memory storage in the association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both learning rules and distribution of stored patterns are inferred from distributions of visual responses for novel and familiar images in the inferior temporal cortex (ITC). Unlike classical attractor neural network models, our model exhibits graded activity in retrieval states, with distributions of firing rates that are close to lognormal. Inferred learning rules are close to maximizing the number of stored patterns within a family of unsupervised Hebbian learning rules, suggesting that learning rules in ITC are optimized to store a large number of attractor states. Finally, we show that there exist two types of retrieval states: one in which firing rates are constant in time and another in which firing rates fluctuate chaotically." @default.
- W2768034240 created "2017-11-17" @default.
- W2768034240 creator A5012600410 @default.
- W2768034240 creator A5047235486 @default.
- W2768034240 date "2018-07-01" @default.
- W2768034240 modified "2023-10-17" @default.
- W2768034240 title "Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data" @default.
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- W2768034240 doi "https://doi.org/10.1016/j.neuron.2018.05.038" @default.
- W2768034240 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6091895" @default.
- W2768034240 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29909997" @default.
- W2768034240 hasPublicationYear "2018" @default.
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