1. LTP LTD LTP LTD High/Correlated Low/uncorrelated High Calcium Moderate
activity
Low/uncorrelated
activity
High NMDA-R
activation
Moderate NMDA-R
activation
High
Calcium
Moderate
Calcium
Magic
LTP
LTD

2. What changes during synaptic plasticity?
What is the mechanism responsible for the induction of synaptic plasticity? (magic?)
Can every form of plasticity be accounted for by STDP?
What are the rules governing synaptic plasticity?
How is synaptic plasticity maintained?

3. What can change during synaptic plasticity?
Presynaptic release probability
The number of postsynaptic receptors.
Properties of postsynaptic receptors

4. Possible evidence for a presynaptic mechanism
Change in failure rate (minimal stimulation)
2. Change in paired pulse ratio
(explain on board – for both ppf and ppd)
3. The MK 801 test

5. Probability of failure:
K vesicles, Pr – prob of release

6. Reminder: short term synaptic dynamics:
depression facilitation

7. Are there other possible reasons for change in PPR?Nu Nr
1/τu
Postsynaptic
spine
Are there other possible reasons for change in PPR?

8. What would happen when we have PPF?

9. Evidence for postsynaptic change:
No change in failures
No change in PPR
No change in NMDA-R component
Different change for AMPA and NMDA-R currents
No change in MK-801

10. The story of silent synapses
Concepts
Minimal stimulation
Effect of depolarization on NMDA-R

12. Model of synaptic plasticity

13. Mechanisms for the induction of synaptic plasticity
Phosphorylation of receptors
Phosphatases, Kinases and Calcium
How do we model the Phosphorylation cycle
Receptor trafficking
Receptor trafficking and Phosphorylation

14. Many are composed of GluR1 and GluR2
Phosphorylation state of Gultamate receptors is correlated with LTP and LTD
GluR1-4, functional units are heteromers, probably composed of 4 subunits, probably composes of different subtypes.
Many are composed of GluR1 and GluR2 R2 P R1 R1 P R2

15. Protein Phosphorylation
Non-phosphorylated
Phosphorylated
Phosphorylation at s831 and s845 both increase conductance but in different ways

16. LTD- dephosphorylation at ser 845
Lee et al. 2000

17. LTP- phosphorylation at ser 831

18. Trafficking of Glutamate receptors constitutive and activity dependent.
Activity dependent insertion and removal and its dependence on Phosphorylation

19. Malinow, Malenka 2002

21. There are two trafficking pathways:
1- Short, in which there is constant plasticity independent trafficking. But dephosphorylation at ser 880 on GluR2 might still trigger LTD.
2- Long, in which phosphorylation triggers LTP.
Note – Phosphorylation also increases conductance directly

22. LTP LTD High/Correlated Low/uncorrelated High Calcium Moderate Calcium
activity
Low/uncorrelated
activity
High
Calcium
Moderate
Calcium
Magic
Phosphorylation
Increased
conductance
AMPAR number
Dephosphorylation
decreased
conductance
AMPAR number

23. The next two topics will be:
From activity to calcium
“Magic” – from calcium to phosphorylation: the signal transduction pathways
Keep in mind, as complex as it might seem to you, it is actually much more complex. This is a cartoon version, passed through my subjective filters
(the end)

24. Here a picture of a spine, with sources and sinks of calcium
NMDAR
VGCC
Release from internal
stores
Sinks
Diffusion
Buffers
Pumps

25. Calcium through
NMDAR

26. For calcium channels the more precise formulation is to use the GHK equation (See Johnston and Wu pg: )
However, for simplicity we will use the simple ‘Ohmic’ formulation:
jCa

27. Ligand binding kinetics – sum of two exponentials with different time constants (Carmignoto and Vicini, 1992)
Calcium Dynamics- first order ODE
NMDA receptor kinetics- sum
of two exponents
0.7
0.5
0.0 ms Ca 25 t

28. Show calcium transients at low and high postsynatic voltage.
Talks about NMDA-R as a coincidence detector

29. A brief summary of the signal transduction pathway leading from Calcium to Phosphorylation/ Dephosphorylation
Magic =

31. Summary

32. How can we
Model the activation of different kinases and phosphatases mathematically?
How can we model phosphorlation and dephophorylation by these enzymes?
Do we have any hope of modeling such a complex system?
Is there a simpler way?