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 |
|