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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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Presentation Transcript
slide1

Magic

Magic

LTP

LTD

High/Correlated

activity

Low/uncorrelated

activity

High NMDA-R

activation

Moderate NMDA-R

activation

High

Calcium

Moderate

Calcium

LTP

LTD

slide2

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?
slide3

What can change during synaptic plasticity?

  • Presynaptic release probability
  • The number of postsynaptic receptors.
  • Properties of postsynaptic receptors
slide4

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
slide5

Probability of failure:

K vesicles, Pr – prob of release

slide7

Nr

Nu

1/τu

Postsynaptic

spine

Are there other possible reasons for change in PPR?

slide9

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
slide10

The story of silent synapses

  • Concepts
  • Minimal stimulation
  • Effect of depolarization on NMDA-R
slide13

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
slide14

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

slide15

Protein Phosphorylation

Non-phosphorylated

Phosphorylated

Phosphorylation at s831 and s845 both increase conductance but in different ways

slide18

Trafficking of Glutamate receptors constitutive and activity dependent.

Activity dependent insertion and removal and its dependence on Phosphorylation

slide21

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

slide22

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

slide23

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)
slide24

Here a picture of a spine, with sources and sinks of calcium

  • Sources
  • NMDAR
  • VGCC
  • Release from internal
  • stores
  • Sinks
  • Diffusion
  • Buffers
  • Pumps
slide26

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

slide27

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

slide28

Show calcium transients at low and high postsynatic voltage.

Talks about NMDA-R as a coincidence detector

slide29

A brief summary of the signal transduction pathway leading from Calcium to Phosphorylation/ Dephosphorylation

Magic

=

slide32

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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