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What does the synapse tell the axon?

What does the synapse tell the axon?. Idan Segev Interdisciplinary Center for Neural Computation Hebrew University. Thanks: Miki London Galit Fuhrman Adi Shraibman Elad Schneidman. Outline. Introduction Questions in my lab. A brief history of the synapse and of “ synaptic efficacy ”

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What does the synapse tell the axon?

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  1. What does the synapse tell the axon? Idan Segev Interdisciplinary Center for Neural Computation Hebrew University Thanks: Miki London Galit Fuhrman Adi Shraibman Elad Schneidman

  2. Outline • Introduction • Questions in my lab. • A brief history of the synapse and of “synaptic efficacy” • - what does it mean? • Complications with “synaptic efficacy” • Information theory and synaptic efficacy • Basic definitions (entropy, mutual information) • The “Plug & Play” model • Preliminary Results • “Synaptic efficacy” • In simple neuron models • In passive dendritic structures • In excitable dendrites • Conclusions • Future questions

  3. Research focus in my group 1. Neuronal “noise” and input-output properties of neurons. Ion-channels, synaptic noise and AP reliability Optimization of information transmission with noise 2. Nonlinear cable theory. Threshold conditions for excitation in excitable dendrites Active propagation in excitable trees 3. “Learning rules” for ion channels and synapses. How to build a “H&H” axon? How to “read” synaptic plasticity? 4. The synapse: “what does it say”? Could dynamic synapses encode the timing of the pre-synaptic spikes? “Synaptic efficacy” - what does it mean?

  4. THE “Synapse”

  5. Motivation: Single synapse matters

  6. “Synaptic efficacy” • Artificial Neural Networks - synaptic efficacy reduced to a single number, Wij (Jij) • Biophysics - Utilizing the (average) properties of the PSP (peak; rise-time; area, charge …) • Cross-Correlation - Relating the pre-synaptic input to the post-synaptic output (the firing probability). How do synaptic properties affect the cross-correlation?

  7. Complications: Who is more “effective” and by how much? • EPSP peak is equal but the rise time is different ? • EPSP area is equal but the peak is different?

  8. Complications: Background synaptic activity Spontaneous in vivo voltage fluctuations in a neuron from the cat visual cortex L.J. Borg-Graham, C. Monier & Y. Frengac

  9. The “Plug & Play” Model Input “Neuron” Noise Output Mutual Information Input Background Activity Output

  10. Compression, Entropy and Mutual Information Mutual Information Compressed output Spike train given the input Information in the input? 01 10 0 10 0 11 0 11100 Compressed Spike train output 0 10 0 11 0 11100 Entropy 01000010010100100001 Output Spike train 01 00001 001 01 001 00001 01 1 0 • The Mutual Information is the extra bits saved by knowing the input. Known Synaptic Input 01 001 01 001 01 001001 01 • Compression Information estimation • We use the CTW compression algorithm (best known today)

  11. I&F (effect of potentiation) Threshold background x5 Isolated synapse Background synapse

  12. (I&F) - EPSP parameters and the MI Fixed peak Fixed charge

  13. Input Why MI corresponds best to EPSP peak? Less spikes, More accurate Sharp EPSP Smeared EPSP More spikes, Less accurate

  14. Passive Cable with synapses

  15. MI (efficacy) of distal synapses scales with EPSP peak Distal Proximal

  16. MI with Active dendritic currents(Linear synapses) proximal distal intermediate distal

  17. Conclusions • Peak EPSP is the dominant parameter for Mutual information of synaptic input • Validity & Generality of method • Advantage of modeling for such issues • Possibility to ask many questions (with control) • applicability for experimental data

  18. Future Questions • Natural Generalizations • Dendritic trees • MI of Inhibitory synapses • Depressing and facilitating synapses • Other noise sources • Efficacy of inhibitory synapse • “Selfish” or Cooperative strategies for maximizing information transfer (each synapse may want to increase each EPSP peak, but others do too) • Establishing and improving the method (confidence limits, better estimates …)

  19. galit

  20. ? ? ? 1 2 3 ? AP Stochastic Model For Dynamic Synapses: 2 types of “randomness”: 1. Is there a vesicle in the release site? 2. Would a vesicle be released in response to a presynaptic AP?

  21. How to quantify the relation between the input properties and its efficacy? Which of these two inputs is more efficient? By how much?

  22. Given a sequencegenerated by a source the Shannon McMillan Breiman theorem states that: Entropy estimation 000000100010101010101010101010101001001011…… • Two problems: • The sequence is finite • We don’t know the true probability p of the sequence (we can only estimate it).

  23. Effect of bin size x5 x3 Wide Sharp Wide Sharp Control

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