Memory consolidation from seconds to weeks: a three-stage neural network model with autonomous reinstatement dynamics
2014 (English)In: Frontiers in Computational Neuroscience, ISSN 1662-5188, Vol. 8, 64- p.Article in journal (Refereed) Published
Declarative long-term memories are not created in an instant. Gradual stabilization and temporally shifting dependence of acquired declarative memories in different brain regions called systems consolidation- can be tracked in time by lesion experiments. The observation of temporally graded retrograde amnesia(RA) following hippocampal lesions points to a gradual transfer of memory from hippocampus to neocortical long-term memory. Spontaneous reactivations of hippocampal memories, asobserved in place cell reactivations during slow wave- sleep, are supposed to driven eocortical reinstatements and facilitate this process. We proposea functional neural network implementation of these ideas and further more suggest anextended three-state framework that includes the prefrontal cortex( PFC). It bridges the temporal chasm between working memory percepts on the scale of seconds and consolidated long-term memory on the scale of weeks or months. Wes how that our three-stage model can autonomously produce the necessary stochastic reactivation dynamics for successful episodic memory consolidation. There sulting learning system is shown to exhibit classical memory effects seen in experimental studies, such as retrograde and anterograde amnesia(AA) after simulated hippocampal lesioning; further more the model reproduces peculiar biological findings on memory modulation, such as retrograde facilitation of memory after suppressed acquisition of new longterm memories- similar to the effects of benzodiazepines on memory.
Place, publisher, year, edition, pages
2014. Vol. 8, 64- p.
memory consolidation, working memory, complementary learning systems, synaptic depression, neural adaptation, retrograde amnesia, anterograde amnesia, retrograde facilitation, computational model
Bioinformatics (Computational Biology) Neurology
IdentifiersURN: urn:nbn:se:kth:diva-148619DOI: 10.3389/fncom.2014.00064ISI: 000339052300001ScopusID: 2-s2.0-84903715018OAI: oai:DiVA.org:kth-148619DiVA: diva2:737233
FunderSwedish Research Council, VR-621-2012-3502VinnovaSwedish Foundation for Strategic Research EU, FP7, Seventh Framework Programme, 269921
QC 201408122014-08-122014-08-112014-08-12Bibliographically approved