1 / 31

What does the synapse tell the axon?

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

mercer
Download Presentation

What does the synapse tell the axon?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Outline Introduction Questions in my group A brief history of the synapse what does “synaptic efficacy” mean? Complications with “synaptic efficacy” Information theory (I.T.) and synaptic efficacy Basic definitions (entropy, compression & mutual information) The “noisy input-output” model Preliminary Results “Synaptic efficacy” in the context of I.T. 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(Elad Schneidmann, Miki London) Ion-channels, synaptic noise and AP reliability Optimization of information transmission with noise 2. Nonlinear cable theory(Claude Meunier) 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”? (Miki London, Galit Fuhrman) Could dynamic synapses encode the timing of the pre-synaptic spikes? “Synaptic efficacy” - what does it mean?

  4. The “father” of thesunaptw Syndesm (“connection”) - Sherrington Synapsis (“Clasp”) - Verrall (Greek scholar/Cambridge) “Each synapsis offers an opportunity for a change in the character of nervous impulses, that the impulse as it passes over from the terminal arborescence of an axon into the dendrite of another cell, starts in that dendrite an impulse having character different from its own” Forster and Sherrington, 1897 Sir Charles Scott Sherrington

  5. Whitney Museum Presents: Synapsis Shuffle, a New Masterwork by Robert Rauschenberg Robert Rauschenberg has organized a hodgepodge group of famous names; from the highbrow (Robert Hughes, Chuck Close) to the lowbrow (Martha Stewart, Michael Ovitz) around the not-especially radical idea that anyone can create a Rauschenberg. Each participant chose an image (by lottery) from a total of 52 Rauschenberg transfer photographs, and then created a composition. “Blown Synapses” The result is bland, homogeneous work on an unnecessarily large scale. Perhaps if the project's parameters had been more narrowly defined?say, if each participant were allotted the same five images?these works would offer more insight into the minds of their composers. As it is, Rauschenberg's shuffle dulls the synapses. Karen Rosenberg”

  6. Motivation: Single synapse matters 400 ext. (10/sec) 100 inh. (65/sec) Mainen & Sejnowki model

  7. Motivation: Single synapse matters 200 sec simulation (10 spikes/sec)

  8. Motivation: Single synapse matters

  9. “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). But how to interpret the shape of the cross-correlation?

  10. Complications with “synaptic efficacy”: PSP have different shape indices: Who is more “effective” and by how much? • EPSP peak is equal but the rise time is different • EPSP area (charge) is equal but the peak is different

  11. Complications with “synaptic efficacy”: Synapses are dynamic Facilitating Depressing

  12. Complications with “synaptic efficacy”:The synapse: a voice in the crowdsynaptic effect depends on the context(and the synapse itself is probabilistic) Spontaneous in vivo voltage fluctuations in a neuron from the cat visual cortex L.J. Borg-Graham, C. Monier & Y. Frengac

  13. A new definition for “Synaptic efficacy” “Synaptic efficacy”: The mutual information between the input and the output ? Input Output “Neuron” Noise Mutual information: what does the synaptic input tell us about the spike output? Mutual Information Input Background Activity Output

  14. Computing the mutual information(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 (MI) is the extra bits saved in encoding the output 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)

  15. Mutual information in a Simple I&F model(effect of potentiation) Output spike train Threshold x5 background Isolated synapse Background synapse

  16. Which of the EPSP parameters affects the MI? Fixed charge Fixed peak the MI corresponds best to the EPSP peak

  17. Input Why the MI corresponds best to EPSP peak? Sharp EPSP Less spikes, More accurate Broad EPSP More spikes, Less accurate

  18. M.I (“synaptic efficacy”) in realistic models: Passive Cable with (linear) synapses +H&H axon

  19. (Cable with linear synapses)MI (synaptic efficacy) of distal synapses scales with EPSP peak Distal Proximal

  20. MI with Active dendritic currents proximal distal intermediate distal The active boosting affects both input synapse but also the “background noise” (i)Proximal synapse transmits less information compared to passive case (“noise” is larger and proximal EPSP is almost passive) (ii) Distal synapse is relatively more boosted due to large local input impedance. (iii) Intermediate synapse is boosted as much as the noise does; so it does not transmit more information in the active case.

  21. Conclusions • The mutual information measure provides a functional link between the synaptic input and the spike output. Hence, the M.I could be interpreted as “synaptic efficacy”. • The EPSP peak (rather than its area) corresponds most closely to the mutual information. • “Synaptic efficacy” depends on the context within which the synapse operates. • Active dendritic currents affect both the “background noise”and the input synapse. The relative effect of this noise on the “efficacy of the synaptic input” depends on the location of the input. Typically, distal synapses tend to be relatively more boosted.

  22. Future Questions Natural Generalizations for charaterizing “synaptic efficacy” *MI (efficacy) of Inhibitory synapses *Depressing, facilitating and probabilistic synapses *Dependence on input structure (regular input; bursting input) *Dependence on the Context (correlated background) *Dependence on dendrtic excitability (Ih, IA, ICa, .., ) *Dependence on # of and site of connection “synaptic efficacy” for many pre-synaptic inputs “Selfish” or Cooperative strategies for maximizing information transfer (each synapse may want to increase its own EPSP peak, but others do too)

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

  24. 12,000 Na channels 3,600 K channels 200mm2

More Related