서울대학교 중앙도서관
S-Space 소개
My S-Space
로그인이 필요합니다.
Login

S-Space

Publications

Detailed Information

Short-Term Synaptic Plasticity and Persistent Activity in the Prefrontal Cortex

DC Field Value Language
dc.contributor.advisor이석호-
dc.contributor.advisor최석우-
dc.contributor.author윤재영-
dc.date.accessioned2019-05-07T06:57:46Z-
dc.date.available2019-05-07T06:57:46Z-
dc.date.issued2019-02-
dc.identifier.other000000153636-
dc.identifier.urihttps://hdl.handle.net/10371/152855-
dc.description학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 생명과학부, 2019. 2. 이석호-
dc.description최석우.-
dc.description.abstractPersistent activity of cue-representing neurons in the prefrontal cortex (PFC) is regarded as a neural basis for working memory. Contribution of short-term synaptic plasticity (STP) at different types of synapses comprising the cortical network to persistent activity, however, remains unclear. Upon characterization of STP at synapses of the PFC network, PFC synapses were found to exhibit distinct STP patterns according to presynaptic and postsynaptic identities. Excitatory synapses from corticopontine (Cpn) neurons were well sustained throughout continued activity, with stronger depression at synapses onto fast-spiking interneurons than those onto pyramidal cells. Inhibitory postsynaptic currents were sustained at a weaker level compared to excitatory postsynaptic currents from Cpn synapses. Computational modeling of a balanced network incorporating empirically observed STP revealed that little depression at recurrent excitatory synapses, combined with stronger depression at other synapses, could provide the PFC with a unique synaptic mechanism for the generation and maintenance of persistent activity.-
dc.description.tableofcontentsAbstract - 1



Table of Contents - 3



Introduction - 8



Methods - 14

Virus injection - 14

Slice preparation - 15

Whole-cell patch clamp - 15

Synaptic stimulation - 17

Quantal analysis - 19

Data analysis - 21

Synaptic vesicle dynamics model - 22

Determining the initial parameters for model fits to STP data at depressing synapses - 24

Determining the initial parameters for model fits to STP data at facilitating synapses - 27

Network model based on conductance synaptic inputs - 30

Network model based on voltage synaptic inputs - 35



Results - 39

EPSC at Cpn synapses are well sustained throughout prolonged activity - 39

Com and thalamocortical synapses are largely depressive - 41

Inhibitory synapses maintain steady-state activity comparable to Cpn synapses - 43

Individual PFC neurons receive proportional levels of excitation and inhibition - 44

Estimation of quantal parameters at PFC synapses - 46

Contribution of STP in the PFC network to persistent activity - 49

Numerical analysis of the spiking network model - 51



Discussion - 59

STP and bistability as alternative or complementary mechanisms for persistent activity in a balanced network - 59

Network organization and persistent activity - 62

Effects of neuromodulation on STP and persistent activity - 63



Figures 1-20 and Tables 1-5 - 65

Figure 1. Experimental design - 66

Figure 2. Intrinsic membrane properties of PFC neurons - 68

Figure 3. Consistency of ChIEF activation - 70

Figure 4. PSC kinetics - 72

Figure 5. STP at Cpn synapses - 74

Figure 6. STP at Com synapses - 76

Figure 7. STP at MDT synapses - 78

Figure 8. STP at somatosensory cortex synapses - 80

Figure 9. STP at IN synapses - 82

Figure 10. Excitation-inhibition ratio at PFC neurons - 84

Figure 11. Quantal parameters at Cpn and IN synapses - 86

Figure 12. Quantal parameters at Com synapses - 88

Figure 13. Quantal current measurements from Sr2+-induced asynchronous release - 90

Figure 14. Simple vesicle dynamics model - 92

Figure 15. Network model of spiking neurons - 94

Figure 16. Extended simulation of the network model - 96

Figure 17. Network model analysis - 98

Figure 18. Network model based on voltage synaptic inputs - 100

Figure 19. Network model behaviors with different forms of STP - 102

Figure 20. Determination of STP model parameters - 104

Table 1. Parameters for the synaptic vesicle dynamics model - 106

Table 2. Parameters for the network model of spiking neurons - 107

Table 3. Additional Parameters for the network model of spiking neurons based on voltage synaptic inputs - 108

Table 4. Notations used for the synaptic vesicle dynamics model - 109

Table 5. Notations used for the network model - 110



References - 111		-
dc.language.isoeng-
dc.publisher서울대학교 대학원-
dc.subject.ddc570-
dc.titleShort-Term Synaptic Plasticity and Persistent Activity in the Prefrontal Cortex-
dc.typeThesis-
dc.typeDissertation-
dc.contributor.AlternativeAuthorJae Young Yoon-
dc.description.degreeDoctor-
dc.contributor.affiliation자연과학대학 생명과학부-
dc.date.awarded2019-02-
dc.contributor.major신경생리학-
dc.identifier.uciI804:11032-000000153636-
dc.identifier.holdings000000000026▲000000000039▲000000153636▲-
Appears in Collections:
Files in This Item:

Altmetrics

Item View & Download Count

Show Simple Item Record
Find it @ SNU

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share

qrcode
SNS Share