Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data.

dc.contributor.author

Pereira, Ulises

dc.contributor.author

Brunel, Nicolas

dc.date.accessioned

2021-06-06T15:56:27Z

dc.date.available

2021-06-06T15:56:27Z

dc.date.issued

2018-07

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2021-06-06T15:56:20Z

dc.description.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.

dc.identifier

S0896-6273(18)30436-7

dc.identifier.issn

0896-6273

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1097-4199

dc.identifier.uri

https://hdl.handle.net/10161/23348

dc.language

eng

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Elsevier BV

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Neuron

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10.1016/j.neuron.2018.05.038

dc.subject

Temporal Lobe

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Animals

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Learning

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Association Learning

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Memory

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Neuronal Plasticity

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Neural Networks, Computer

dc.title

Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data.

dc.type

Journal article

pubs.begin-page

227

pubs.end-page

238.e4

pubs.issue

1

pubs.organisational-group

School of Medicine

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Physics

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Neurobiology

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Duke Institute for Brain Sciences

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Center for Cognitive Neuroscience

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Duke

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Trinity College of Arts & Sciences

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Basic Science Departments

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University Institutes and Centers

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Institutes and Provost's Academic Units

pubs.publication-status

Published

pubs.volume

99

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