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Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.

dc.contributor.author Gillett, Maxwell
dc.contributor.author Pereira, Ulises
dc.contributor.author Brunel, Nicolas
dc.date.accessioned 2021-06-06T15:50:02Z
dc.date.available 2021-06-06T15:50:02Z
dc.date.issued 2020-11-11
dc.identifier 1918674117
dc.identifier.issn 0027-8424
dc.identifier.issn 1091-6490
dc.identifier.uri https://hdl.handle.net/10161/23343
dc.description.abstract Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.
dc.language eng
dc.publisher Proceedings of the National Academy of Sciences
dc.relation.ispartof Proceedings of the National Academy of Sciences of the United States of America
dc.relation.isversionof 10.1073/pnas.1918674117
dc.subject Hippocampus
dc.subject Parietal Lobe
dc.subject Nerve Net
dc.subject Neurons
dc.subject Animals
dc.subject Mice
dc.subject Learning
dc.subject Neuronal Plasticity
dc.subject Models, Neurological
dc.subject Computer Simulation
dc.subject Neural Networks, Computer
dc.title Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning.
dc.type Journal article
duke.contributor.id Brunel, Nicolas|0785756
dc.date.updated 2021-06-06T15:50:00Z
pubs.begin-page 29948
pubs.end-page 29958
pubs.issue 47
pubs.organisational-group School of Medicine
pubs.organisational-group Physics
pubs.organisational-group Neurobiology
pubs.organisational-group Duke Institute for Brain Sciences
pubs.organisational-group Center for Cognitive Neuroscience
pubs.organisational-group Duke
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group Basic Science Departments
pubs.organisational-group University Institutes and Centers
pubs.organisational-group Institutes and Provost's Academic Units
pubs.publication-status Published
pubs.volume 117


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