Idan SegevInterdisciplinary Center for Neural Computation

Hebrew University

Thanks to:Miki LondonGalit FuhrmanAdi ShraibmanElad Schneidman

What does the synapse tell the axon?

OutlineIntroduction

Questions in my groupA brief history of the synapsewhat does “synaptic efficacy” mean?Complications with “synaptic efficacy”

Information theory (I.T.) and synaptic efficacyBasic definitions (entropy, compression & mutual information)The “noisy input-output” model

Preliminary Results“Synaptic efficacy” in the context of I.T.

In simple neuron modelsIn passive dendritic structuresIn excitable dendrites

ConclusionsFuture questions

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 dendritesActive 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?Synaptic efficacy” - what does it mean?

Research focus in my group

The “father” of the

Sir Charles Scott Sherrington

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

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”

Motivation: Single synapse matters

400 ext. (10/sec)100 inh. (65/sec)

Mainen & Sejnowki model

Motivation: Single synapse matters

200 sec simulation (10 spikes/sec)

Motivation: Single synapse matters

“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?

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

Complications with “synaptic efficacy”:

Synapses are dynamic

Facilitating

Depressing

Complications with “synaptic efficacy”: The synapse: a voice in the crowd

synaptic effect depends on the context(and the synapse itself is probabilistic)

L.J. Borg-Graham, C. Monier & Y. Frengac

Spontaneous in vivo voltage fluctuations

in a neuron from the cat visual cortex

A new definition for “Synaptic efficacy”

“Neuron”OutputInput

Noise

Background Activity

Input

Output

MutualInformation

Mutual information: what does the synaptic input tell us about the spike output?

“Synaptic efficacy”: The mutual information between the input and the output

?

0 10 0 11 0 11 10 0 Entropy

Known Synaptic Input

01 001 01 001 01 001 001 01 0 1 1 0• The Mutual Information (MI) is the extra bits saved in encoding the

output by knowing the input.

01000010010100100001

Computing the mutual information(Compression, Entropy and Mutual Information)

0 10 0 11 0 11 10 0

01 000 01 001 01 001 000 01

Information in the input?

Output Spike train

Compressed Spike train output

0 1 1 0

Compressed output Spike train given the input

Mutual Information

• Compression Information estimation

• We use the CTW compression algorithm (best known today)

Mutual information in a Simple I&F model

(effect of potentiation)

Threshold

Isolated synapse background

Background synapse

x5

Output spike train

Which of the EPSP parameters affects the MI?

Fixed peak Fixed charge

the MI corresponds best to the EPSP peak

Why the MI corresponds best to EPSP peak?

Sharp EPSP

Broad EPSP

Less spikes, More accurate

More spikes, Less accurate

Input

M.I (“synaptic efficacy”) in realistic models:

Passive Cable with (linear) synapses +H&H axon

(Cable with linear synapses)

MI (synaptic efficacy) of distal

synapses scales with EPSP peak

ProximalDistal

MI with Active dendritic currents

proximal

distal

distal

intermediate

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.

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”.

• “Synaptic efficacy” depends on the context within which the synapse operates.

• The EPSP peak (rather than its area) corresponds most closely to the mutual information.

• 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.

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)

Effect of bin size

SharpWide

WideSharp

Control

x3

x5

12,000 Na channels3,600 K channels

200 m2