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CREB and neuronal selection for memory trace

期刊

FRONTIERS IN NEURAL CIRCUITS
卷 7, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fncir.2013.00044

关键词

neural network; memory trace; neuronal selection; CREB; excitability; dendritic spine

资金

  1. National Research Foundation of Korea (NRF)
  2. Korea government (MEST) [2011-0005755, 2011-0013173]
  3. BK21 project of Korean Research Foundation
  4. project of Global Ph.D. Fellowship
  5. World Class Institute (WCI) program of the National Research Foundation of Korea (NRF)
  6. Ministry of Education, Science, and Technology (MEST) [WCI 2009-003]
  7. National Research Foundation of Korea [2011-0013173] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

Despite considerable progress over the past several decades, our understanding of the mechanisms underlying memory encoding, storage, and expression in a complex neural network are far from complete. In particular, how some neurons rather than others are selectively engaged to encode memory remains largely unknown. Using virus-mediated gene delivery into a small subset of neurons in a given network, molecular imaging of neuronal activity, pharmacological perturbation of specific neurons' activity and animal behavior assays, recent studies have begun to provide insight into molecular and cellular mechanisms responsible for the selection of neurons for inclusion into a memory trace. Here, we focus on a review of recent findings supporting the hypothesis that the level of the transcription factor CREB (cAMP/Ca2+-response element binding protein) is a key factor governing which neurons are recruited to a given memory trace. These recent findings open a new perspective on memory trace at the neural circuit level and also raise many important questions. Future studies employing more advanced neurobiological techniques for targeting defined populations of neurons and manipulating their activity in time and space in a complex neural network will give answers to these newly emerging questions and extend our understanding of the neurobiological basis of the memory trace.

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