Abstract
Extensive work in computational modeling has highlighted the advantages for employing sparse yet distributed data representation and storage Kanerva (1998), properties that extend to neuronal networks encoding mnemonic information (memory traces or engrams). While neurons that participate in an engram are distributed across multiple brain regions, within each region, the cellular sparsity of the mnemonic representation appears to be quite fixed. Although technological advances have enabled significant progress in identifying and manipulating engrams, relatively little is known about the region-dependent microcircuit rules governing the cellular sparsity of an engram. Here we review recent studies examining the mechanisms that help shape engram architecture and examine how these processes may regulate memory function. We speculate that countervailing forces in local microcircuits contribute to the generation and maintenance of engrams and discuss emerging questions regarding how engrams are formed, stored and used.
| Original language | English |
|---|---|
| Pages (from-to) | 163-170 |
| Number of pages | 8 |
| Journal | Current Opinion in Neurobiology |
| Volume | 54 |
| Early online date | 10 Nov 2018 |
| DOIs | |
| Publication status | Published - Feb 2019 |
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Neuronal competition : microcircuit mechanisms define the sparsity of the engram. / Rao-Ruiz, Priyanka; Yu, Julia; Kushner, Steven A.; Josselyn, Sheena A.
In: Current Opinion in Neurobiology, Vol. 54, 02.2019, p. 163-170.Research output: Contribution to Journal › Review article › Academic › peer-review
TY - JOUR
T1 - Neuronal competition
T2 - microcircuit mechanisms define the sparsity of the engram
AU - Rao-Ruiz, Priyanka
AU - Yu, Julia
AU - Kushner, Steven A.
AU - Josselyn, Sheena A.
PY - 2019/2
Y1 - 2019/2
N2 - Extensive work in computational modeling has highlighted the advantages for employing sparse yet distributed data representation and storage Kanerva (1998), properties that extend to neuronal networks encoding mnemonic information (memory traces or engrams). While neurons that participate in an engram are distributed across multiple brain regions, within each region, the cellular sparsity of the mnemonic representation appears to be quite fixed. Although technological advances have enabled significant progress in identifying and manipulating engrams, relatively little is known about the region-dependent microcircuit rules governing the cellular sparsity of an engram. Here we review recent studies examining the mechanisms that help shape engram architecture and examine how these processes may regulate memory function. We speculate that countervailing forces in local microcircuits contribute to the generation and maintenance of engrams and discuss emerging questions regarding how engrams are formed, stored and used.
AB - Extensive work in computational modeling has highlighted the advantages for employing sparse yet distributed data representation and storage Kanerva (1998), properties that extend to neuronal networks encoding mnemonic information (memory traces or engrams). While neurons that participate in an engram are distributed across multiple brain regions, within each region, the cellular sparsity of the mnemonic representation appears to be quite fixed. Although technological advances have enabled significant progress in identifying and manipulating engrams, relatively little is known about the region-dependent microcircuit rules governing the cellular sparsity of an engram. Here we review recent studies examining the mechanisms that help shape engram architecture and examine how these processes may regulate memory function. We speculate that countervailing forces in local microcircuits contribute to the generation and maintenance of engrams and discuss emerging questions regarding how engrams are formed, stored and used.
UR - http://www.scopus.com/inward/record.url?scp=85056238592&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056238592&partnerID=8YFLogxK
U2 - 10.1016/j.conb.2018.10.013
DO - 10.1016/j.conb.2018.10.013
M3 - Review article
VL - 54
SP - 163
EP - 170
JO - Current Opinion in Neurobiology
JF - Current Opinion in Neurobiology
SN - 0959-4388
ER -