Literature DB >> 21854962

Hippocampal-cortical interactions and the dynamics of memory trace reactivation.

C Daniela Schwindel1, Bruce L McNaughton.   

Abstract

The formation of memory and extraction of knowledge from it is the basis of intelligence. It is believed that, during slow-wave sleep, the brain reorganizes its connectivity matrix so as to store new information optimally. As the probability of direct synaptic connection between arbitrarily chosen neurons in the cortex is extremely low (on the order of 10(- 6)), a combination of modular and hierarchical organization appears to be necessary to enable rapid association of arbitrary items. During waking, an "index" of the neural pattern in lower order cortical modules may be created and stored in the highest order association cortex, the hippocampus, and broadcast back to the relevant cortical modules, where it is stored with the local data. In this manner, the pattern can be spontaneously reactivated and reinstated in all modules to enable the establishment of crossmodular connections, and replay of such patterns of neural activity or "phase sequences" has been observed in hippocampus and neocortex. In prefrontal cortex, the playback of "phase sequences" is associated with periods of intense upstate/downstate transitions and can be accelerated five- to eightfold relative to the waking state. The playback speed declines over time as does the strength of the replay, which is consistent with a simple decay of an asymmetric component of the synaptic weight matrix induced during the experience itself. Since the hippocampal events associated with memory reactivation (sharp-wave ripple events) tend to be correlated with up transitions in the neocortex, hippocampus may coordinate reactivation in neocortex, at least under some conditions.
Copyright © 2011 Elsevier B.V. All rights reserved.

Mesh:

Year:  2011        PMID: 21854962     DOI: 10.1016/B978-0-444-53839-0.00011-9

Source DB:  PubMed          Journal:  Prog Brain Res        ISSN: 0079-6123            Impact factor:   2.453


  41 in total

1.  Offline reactivation of experience-dependent neuronal firing patterns in the rat ventral tegmental area.

Authors:  José L Valdés; Bruce L McNaughton; Jean-Marc Fellous
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2.  Intra- and interregional cortical interactions related to sharp-wave ripples and dentate spikes.

Authors:  Drew B Headley; Vasiliki Kanta; Denis Paré
Journal:  J Neurophysiol       Date:  2016-11-09       Impact factor: 2.714

3.  Coordinated activities of retrosplenial ensembles during resting-state encode spatial landmarks.

Authors:  HaoRan Chang; Ingrid M Esteves; Adam R Neumann; Jianjun Sun; Majid H Mohajerani; Bruce L McNaughton
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4.  Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks.

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Journal:  J Comput Neurosci       Date:  2012-10-04       Impact factor: 1.621

5.  Choice for Drug or Natural Reward Engages Largely Overlapping Neuronal Ensembles in the Infralimbic Prefrontal Cortex.

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Review 6.  Using c-fos to study neuronal ensembles in corticostriatal circuitry of addiction.

Authors:  Fabio C Cruz; F Javier Rubio; Bruce T Hope
Journal:  Brain Res       Date:  2014-11-11       Impact factor: 3.252

Review 7.  Unraveling the Evolutionary Determinants of Sleep.

Authors:  William J Joiner
Journal:  Curr Biol       Date:  2016-10-24       Impact factor: 10.834

8.  Large time step discrete-time modeling of sharp wave activity in hippocampal area CA3.

Authors:  Paola Malerba; Nikolai F Rulkov; Maxim Bazhenov
Journal:  Commun Nonlinear Sci Numer Simul       Date:  2018-12-20       Impact factor: 4.260

9.  Circuit mechanisms of hippocampal reactivation during sleep.

Authors:  Paola Malerba; Maxim Bazhenov
Journal:  Neurobiol Learn Mem       Date:  2018-05-01       Impact factor: 2.877

Review 10.  New technologies for examining the role of neuronal ensembles in drug addiction and fear.

Authors:  Fabio C Cruz; Eisuke Koya; Danielle H Guez-Barber; Jennifer M Bossert; Carl R Lupica; Yavin Shaham; Bruce T Hope
Journal:  Nat Rev Neurosci       Date:  2013-10-03       Impact factor: 34.870

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