1. Spike Timing-Dependent Plasticity Presented by: Arash Ashari Slides mostly from: www.mbi.osu.edu 1  Woodin MA, Ganguly K, and Poo MM. Coincident pre- and postsynaptic activity modifies GABAergic synapses by postsynaptic changes in Cl - transporter activity, Neuron 39: 807–820, 2003.  Dan Y, Poo MM. Spike timing-dependent plasticity: from synapse to perception. Physiol Rev. 2006 Jul;86(3):1033-48. Review.  Rao, R. and Sejnowski, T. Spike-timing-dependent Hebbian plasticity as temporal difference learning. Neural Comput, 13(10):2221–2237, 2001.

2. Outline Neuron, Synapse, Depolarization and Hyperpolarization Long-Term Potentiation and Depression (LTP and LTD) Spike Timing-Dependent Plasticity (STDP) A Mathematical Model for STDP Discussion 2

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5. Dendrite: 1. Spatial Summation 2. Temporal Summation 5

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8. Neurotransmitters Amino Acids … Glutamate, GABA Biogenic Amines … Dopamine, Histamine Neuropeptides … LHRH, Proctolin 8

9. Neurotransmitters Amino Acids … Glutamate, GABA Biogenic Amines … Dopamine, Histamine Neuropeptides … LHRH, Proctolin NMDA, GABA A are typical receptors Glumatergic: Excitatory GABAergic: Inhabitatory 9

10. Excitatory (EPSP) or Inhibitory (IPSP) 10

11. Depolarization: Influx of Na and Ca cations This causes a spike (Action Potential – a pulse-like wave of voltage) Hyperpolarization: Outflux of K cations or influx of Cl anion 11

12. Long-Term Synaptic Enhancement Long-Term Potentiation (LTP) ~ Rapid and sustained increase in synaptic efficacy following a brief but potent stimulus Best studied in the hippocampus Induction of LTP occurs at the postsynaptic site and requires the conjunction of pre and post-synaptic activity On the order of hours, days or longer 12

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14. Long-term Synaptic Enhancement The mechanisms underlying LTP remain controversial The existence of Long Term Depression (LTD) A possible way to study LTP might be Spike Time Dependent Plasticity (STDP) 14

15. Spike Time Dependent Plasticity 15

16. Synaptic Plasticity Hebb’s Postulate: When an axon of cell A... excites cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells so that A's efficiency as one of the cells firing B is increased. In standard Hebbian learning, a synaptic weight is increased if presynaptic and postsynaptic neuron are `simultaneously' active. If neurons communicate by spikes, the concept of simultaneity implies the pre- and postsynaptic spikes occur within some time window. Theory predicts that these time windows could have two phases corresponding to an increase (potentitiation) or decrease (depresseion) of the synaptic weight depending on the relative timing of pre- and postsynaptic spike. Such asymmetric learning rules with two phases have been found in recent experiments. 16

17. Spike Time Dependent Plasticity t t StrengtheningWeakening Presynaptic Cell Postsynaptic Cell Excitatory Synapse 17

18. What do experiments show?  t = t post - t pre Guo-qiang Bi and Mu-ming Poo, J. of Neuroscience, December 1998 Presynaptic Cell Postsynaptic Cell Excitatory Synapse % Change in PostSynaptic Current 18

19. So, what are the STDP rules? L. F. Abbott and S. B. Nelson, 2000 Nature Review 19

20. (ms) Similar results: Karmarkar and Bunomano, 2002; Abarbanel et. al. 2003; Kitijima and Hara, 2000 20

21. -50050100150200 0.02.04.06.08 0.1  t (ms) I peak +/- (  M / ms)  B  N LTD LTP LTD LTP 21

22. A Mathematical Model for STDP g ex is PLASTIC Presynaptic Cell Postsynaptic Cell Excitatory Synapse V  Postsynaptic Membrane Potential V rest  Postsynaptic Membrane Resting Potential g ex  Excitatory Synaptic Conductance E ex  Excitatory Synaptic Reversal Potential  m  Membrane Potential decay time constant  ex  gex decay time constant 22

23. A-A- tt F(%) M(t) A+A+ tt F(%) P(t) Song, Miller, Abbott Model for STDP  t = t post - t pre t t Strengthening t Weakening t 23

24. When Presynaptic cell fires,  Update P Updating P and M When Postsynaptic cell fires,  Update M Presynaptic Cell Postsynaptic Cell Excitatory Synapse A+A+ tt F(%) P(t) A-A- tt F(%) M(t) 24

25. Denotes the peak synaptic conductance (the synaptic conductance immediately after an isolated presynaptic spike) When Postsynaptic cell firesWhen Presynaptic cell fires Synaptic conductance g ex is updated when there is a presynaptic action potential at excitatory synapse ADDITIVE RULE for Synaptic Modification Updating g ex 25

26. When Presynaptic cell fires, 1. 2. 3. When Postsynaptic cell fires, 1. 2. Putting things into perspective Presynaptic Cell Postsynaptic Cell Excitatory Synapse t t Strengthening t Weakening t 26

27. In the real world Multiple Synapses 1 postsynaptic neuron How about inhibitory synapses!! Hence, the equation for the postsynaptic neuron changes to include the inhibitory synapses g in  Inhibitory Synaptic Conductance E in  Inhibitory Synaptic Reversal Potential  in  g in decay time constant 27

28. Postsynaptic Cell 1000 Excitatory Synapses 200 Inhibitory Synapses 1000 ‘P’ functions, but only ONE ‘M’ function!!! Presynaptic Excitatory cell fires, 1. 2. 3. Postsynaptic cell fires, 1. 2. Presynaptic Inhibitory cell fires, 1. 28

29. Discussion How LTP and LTD occur? Can STDP underlie Memory and Learning? How? Correlated activity can occur purely by chance, rather should be learned than reflecting a causal relationship that STDP as a Reinforcement Learning/ Temporal Difference Learning 29

30. References  Woodin MA, Ganguly K, and Poo MM. Coincident pre- and postsynaptic activity modifies GABAergic synapses by postsynaptic changes in Cl- transporter activity, Neuron 39: 807–820, 2003.  Dan Y, Poo MM. Spike timing-dependent plasticity: from synapse to perception. Physiol Rev. 2006 Jul;86(3):1033-48. Review.  Rao, R. and Sejnowski, T. Spike-timing-dependent Hebbian plasticity as temporal difference learning. Neural Comput, 13(10):2221–2237, 2001.  Song, S., Miller, K. D., & Abbott, L. F. (2000). Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3, 919-926.  http://en.wikipedia.org/wiki/ 30

31. Thank you Any Questions? 31