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High/Correlated activity

Magic. Magic. LTP. LTD. High/Correlated activity. Low/uncorrelated activity. High NMDA-R activation. Moderate NMDA-R activation. High Calcium. Moderate Calcium. LTP. LTD. What changes during synaptic plasticity?

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High/Correlated activity

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

  11. Model of synaptic plasticity

  12. 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

  13. 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

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

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

  16. LTP- phosphorylation at ser 831

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

  18. Malinow, Malenka 2002

  19. 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

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

  21. 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)

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

  23. Calcium through NMDAR

  24. 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

  25. t » 25 ms Ca • 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

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

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

  28. Summary

  29. 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?

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