PMID- 29765314
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20201001
IS  - 1662-5188 (Print)
IS  - 1662-5188 (Electronic)
IS  - 1662-5188 (Linking)
VI  - 12
DP  - 2018
TI  - Underlying Mechanisms of Cooperativity, Input Specificity, and Associativity of 
      Long-Term Potentiation Through a Positive Feedback of Local Protein Synthesis.
PG  - 25
LID - 10.3389/fncom.2018.00025 [doi]
LID - 25
AB  - Long-term potentiation (LTP) is a specific form of activity-dependent synaptic 
      plasticity that is a leading mechanism of learning and memory in mammals. The 
      properties of cooperativity, input specificity, and associativity are essential 
      for LTP; however, the underlying mechanisms are unclear. Here, based on 
      experimentally observed phenomena, we introduce a computational model of synaptic 
      plasticity in a pyramidal cell to explore the mechanisms responsible for the 
      cooperativity, input specificity, and associativity of LTP. The model is based on 
      molecular processes involved in synaptic plasticity and integrates gene 
      expression involved in the regulation of neuronal activity. In the model, we 
      introduce a local positive feedback loop of protein synthesis at each synapse, 
      which is essential for bimodal response and synapse specificity. Bifurcation 
      analysis of the local positive feedback loop of brain-derived neurotrophic factor 
      (BDNF) signaling illustrates the existence of bistability, which is the basis of 
      LTP induction. The local bifurcation diagram provides guidance for the 
      realization of LTP, and the projection of whole system trajectories onto the 
      two-parameter bifurcation diagram confirms the predictions obtained from 
      bifurcation analysis. Moreover, model analysis shows that pre- and postsynaptic 
      components are required to achieve the three properties of LTP. This study 
      provides insights into the mechanisms underlying the cooperativity, input 
      specificity, and associativity of LTP, and the further construction of neural 
      networks for learning and memory.
FAU - Hao, Lijie
AU  - Hao L
AD  - School of Mathematics and Systems Science, Key Laboratory of Mathematics, 
      Informatics and Behavioral Semantics, Ministry of Education, Beihang University, 
      Beijing, China.
FAU - Yang, Zhuoqin
AU  - Yang Z
AD  - School of Mathematics and Systems Science, Key Laboratory of Mathematics, 
      Informatics and Behavioral Semantics, Ministry of Education, Beihang University, 
      Beijing, China.
FAU - Lei, Jinzhi
AU  - Lei J
AD  - Zhou Pei-Yuan Center for Applied Mathematics, MOE Key Laboratory of 
      Bioinformatics, Tsinghua University, Beijing, China.
LA  - eng
PT  - Journal Article
DEP - 20180501
PL  - Switzerland
TA  - Front Comput Neurosci
JT  - Frontiers in computational neuroscience
JID - 101477956
PMC - PMC5938377
OTO - NOTNLM
OT  - associativity
OT  - cooperativity
OT  - input specificity
OT  - local positive feedback
OT  - long-term potentiation
EDAT- 2018/05/17 06:00
MHDA- 2018/05/17 06:01
PMCR- 2018/01/01
CRDT- 2018/05/17 06:00
PHST- 2017/10/03 00:00 [received]
PHST- 2018/03/28 00:00 [accepted]
PHST- 2018/05/17 06:00 [entrez]
PHST- 2018/05/17 06:00 [pubmed]
PHST- 2018/05/17 06:01 [medline]
PHST- 2018/01/01 00:00 [pmc-release]
AID - 10.3389/fncom.2018.00025 [doi]
PST - epublish
SO  - Front Comput Neurosci. 2018 May 1;12:25. doi: 10.3389/fncom.2018.00025. 
      eCollection 2018.