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Effect of Interpopulation Spike-Timing-Dependent Plasticity on Synchronized Rhythms in Neuronal Networks with Inhibitory and Excitatory Populations S.-Y. Kim and W. Lim Institute for Computational Neuroscience Daegu National University of


slide-1
SLIDE 1

Effect of Interpopulation Spike-Timing-Dependent Plasticity

  • n Synchronized Rhythms in Neuronal Networks with

Inhibitory and Excitatory Populations

S.-Y. Kim and W. Lim Institute for Computational Neuroscience Daegu National University of Education

  • Fast Sparsely Synchronization (FSS)
  • Population level: Fast synchronous oscillations [e.g. gamma rhythm (30~100 Hz) during

awake behaving states and rapid eye movement sleep]

  • Cellular level: Stochastic and intermittent spike discharges at much lower rates than the

population oscillation frequency

  • Related to diverse cognitive functions (e.g. multisensory feature binding, selective

attention, and memory formation)

1

  • Small-World Network (SWN)
  • Architecture of synaptic connections in real brain: Complex topology

neither regular nor completely random

  • SWN: Predominantly local connections and rare long-range

connections โ†’ High local clustering and short average path length

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SLIDE 2

Interpopulation STDPs

2

  • Purpose of Our Study

Investigation of Effect of Interpopulation (I to E and E to I) STDPs on FSS in Clustered SWNs with Two Inhibitory and Excitatory Populations.

  • Synaptic Plasticity
  • Adaptation of synapses in real brain: Synaptic strengths may vary to adapt to

environment (potentiated or depressed)

  • Associated with brain functions (learning, memory, and development) and neural

diseases (Parkinsonโ€™s disease and epilepsy)

  • Spike-Timing-Dependent Plasticity (STDP)
  • STDP: Plasticity depending on the relative time difference between the pre-and

the post-synaptic spike times

  • Study of synaptic plasticity: Mainly focused on excitatory-to-excitatory (E to E) synapses
  • Synaptic plasticity at inhibitory synapse: Less attention due to experimental obstacles

and diversity of inhibitory interneurons. (With the advent of fluorescent labeling and optical manipulation inhibitory synaptic plasticity has begun to be focused.) Particularly studies on inhibitory STDP at inhibitory-to-excitatory (I to E) synapses

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SLIDE 3

Clustered SWNs Composed of Two I- & E-Populations

3

  • Clustered SWNs
  • Watts-Strogatz SWN consisting of ๐‘‚๐ฝ (๐‘‚๐น) (๐‘‚๐น: ๐‘‚๐ฝ = 4: 1)

FS interneurons (RS pyramidal cells)

  • Random connections between two I-SWN & E-SWN
  • Interpopulation (I to E and E to I) STDP
  • Update of synaptic strength: Nearest-spike pair-based STDP rule:
  • Initial interpopulation synaptic strengths: Gaussian distribution with mean ๐พ0

(๐น๐ฝ)=800,

๐พ0

(๐ฝ๐น)=487.5 & standard deviation ๐œ0 = 5

  • Delayed Hebbian I to E iSTDP
  • Anti-Hebbian E to I eSTDP

| ) ( | ) (

) ( ) ( ) ( * ) ( ) ( ) ( XY ij XY ij XY ij XY XY ij XY ij

t J J J J J ๏„ ๏„ โˆ’ + โ†’ ๏ค

๏ƒฏ ๏ƒฎ ๏ƒฏ ๏ƒญ ๏ƒฌ ๏€ผ ๏„ ๏„ ๏€พ ๏„ ๏„ = ๏„

โˆ’ +

for ) ( for ) (

) ( ) ( ) ( ) ( ) ( EI ij EI ij EI ij EI ij EI ij

t t t E t t t E J

๏ข ๏ข

โˆ’ +

๏„ โˆ’ โˆ’ โˆ’ ๏„ โˆ’ + + +

โˆ’ = =

๏ด ๏ด / /

) ( ; ) (

ij ij

t t

e N A t E e N A t E

๏ข ๏ข ๏ข

๏ด ๏ข

+ +

๏ƒ— = e N

๏ข ๏ข ๏ข

๏ด ๏ข

โˆ’ โˆ’

๏ƒ— = e N

๏ƒฏ ๏ƒฎ ๏ƒฏ ๏ƒญ ๏ƒฌ ๏€ผ ๏„ ๏„ ๏€พ ๏„ ๏„ โˆ’ โˆ’ = ๏„

โˆ’ โˆ’ + +

for ) / exp( for ) / exp(

) ( ) ( ) ( ) ( ) ( IE ij IE ij IE ij IE ij IE ij

t t A t t A J ๏ด ๏ด

(cf. I to I iSTDP: Anti-Hebbian STDP) (cf. E to E eSTDP: Hebbian STDP)

ฮ”๐‘ข๐‘—๐‘˜

๐‘Œ๐‘ = ๐‘ข๐‘— (๐‘ž๐‘๐‘ก๐‘ข,๐‘Œ) โˆ’ ๐‘ข๐‘˜ ๐‘ž๐‘ ๐‘“,๐‘ , ๐พ๐‘—๐‘˜ (๐‘Œ๐‘) โˆˆ [๐พ๐‘š(= 0.0001), ๐พโ„Ž(= 2000)]

ฮ”๐‘ข๐‘—๐‘˜

๐น๐ฝ > 0 โ†’ iLTP

, ฮ”๐‘ข๐‘—๐‘˜

๐น๐ฝ < 0 โ†’ iLTD

ฮ”๐‘ข๐‘—๐‘˜

๐ฝ๐น > 0 โ†’ eLTD, ฮ”๐‘ข๐‘—๐‘˜ ๐ฝ๐น < 0 โ†’ eLTP

๐ต+ = 0.4 ๐ตโˆ’ = 0.35 ๐œ+ = 2.6 ๐œโˆ’ = 2.8 ๐›พ = 10 ๐œ€ = 0.1 ๐ต+ = 1.0 ๐ตโˆ’ = 0.9 ๐œ+ = 15.0 ๐œโˆ’ = 15.0 ๐œ€ = 0.05

slide-4
SLIDE 4
  • FSS in the Absence of STDP

Occurrence of FSS in the range of ๐ธ1

โˆ— โ‰ƒ 91 < ๐ธ < ๐ธ2 โˆ— (โ‰ƒ 537)

Long-term Potentiation (LTP) and Depression (LTD)

4

  • Time-Evolution of Population-Averaged Synaptic Strength <๐พ๐‘—๐‘˜

(๐น๐ฝ)> & <๐พ๐‘—๐‘˜ (๐ฝ๐น)>

๐ธ = 110, 250, 400 (intermediate ๐ธ): Monotonic increase (decrease) in <๐พ๐‘—๐‘˜

(๐น๐ฝ)> (<๐พ๐‘—๐‘˜ (๐ฝ๐น)>) above ๐พ0 (๐น๐ฝ) (below ๐พ0 (๐ฝ๐น)) and saturated

limit value โ†’ iLTP (eLTD) ๐ธ = 95, 500, 600: (small & large ๐ธ) Monotonic decrease (increase) in <๐พ๐‘—๐‘˜

(๐น๐ฝ)> (<๐พ๐‘—๐‘˜ (๐ฝ๐น)>) below ๐พ0 (๐น๐ฝ) (above ๐พ0 (๐ฝ๐น)) and saturated

limit value โ†’ iLTD (eLTP)

  • Population-Averaged Saturated Limit Values of

Synaptic Strengths <<๐‘ฒ๐’‹๐’Œ

๐‘ฑ๐‘ญ โˆ—>>r & <<๐‘ฒ๐’‹๐’Œ ๐‘ฑ๐‘ญ โˆ—>>r

Occurrence of iLTP & eLTD in an intermediate region [เทฉ ๐ธ๐‘š โ‰ƒ 99 < ๐ธ < เทฉ ๐ธโ„Ž(โ‰ƒ 408)]: <<๐พ๐‘—๐‘˜

๐ฝ๐น โˆ—>>r : Increase & <<๐พ๐‘—๐‘˜ ๐ฝ๐น โˆ—>>r: Decrease

Otherwise, occurrence of iLTD & eLTP in the regions of small & large ๐ธ : <<๐พ๐‘—๐‘˜

๐ฝ๐น โˆ—>>r : Decrease & <<๐พ๐‘—๐‘˜ ๐ฝ๐น โˆ—>>r: Increase

<<๐พ๐‘—๐‘˜

๐ฝ๐น โˆ—>>r : Bell-shaped graph. <<๐พ๐‘—๐‘˜ ๐ฝ๐น โˆ—>>r: Well-shaped graph.

slide-5
SLIDE 5
  • Raster Plots of Spikes and Instantaneous Population Spike Rates ๐‘บ๐’€ (๐’€ = ๐‘ญ or ๐‘ฑ)

Absence of STDP Presence of interpopulation STDPs ๐ธ = 110, 250, 400 (intermediate ๐ธ) Decrease in degree of FSS (Decrease in amplitudes of ๐‘†๐‘Œ) Due to increased I to E synaptic inhibition (iLTP) and decreased E to I synaptic excitation (eLTD) ๐ธ = 95, 500, 600: (small & large ๐ธ) Increase in degree of FSS (Increase in amplitudes of ๐‘†๐‘Œ) Due to decreased I to E synaptic inhibition (iLTD) and increased E to I synaptic excitation (eLTP)

Effect of the Interpopulation STDPs on the FSS

5

Black: I-population, Gray: E-population

slide-6
SLIDE 6

Equalization Effect in Interpopulation Synaptic Plasticity

6

  • Characterization of Population Behaviors for FSS

FSS โ†’ Successive appearance of sparse spiking stripes in the raster plot of spikes Average occupation degree <๐‘ƒ๐‘—

(๐‘Œ)>: Density of spikes in the spiking stripes

Average pacing degree <๐‘„

๐‘— (๐‘Œ)>: Degree of phase

coherence between spikes Spiking measure ๐‘๐‘ก

(๐‘Œ): Product of <๐‘ƒ๐‘— (๐‘Œ)> & <๐‘„ ๐‘— (๐‘Œ)>

Intermediate ๐ธ region (iLTP & eLTD: Gray region): Decrease in <๐‘ƒ๐‘—

(๐‘Œ)>, <๐‘„ ๐‘— (๐‘Œ)>, & ๐‘๐‘ก (๐‘Œ)

Large & Small ๐ธ regions (iLTD & eLTP): Increase in <๐‘ƒ๐‘—

(๐‘Œ)>, <๐‘„ ๐‘— (๐‘Œ)>, & ๐‘๐‘ก (๐‘Œ)

<๐‘ƒ๐‘—

(๐‘Œ)>: Relatively fast-increasing function

โ†’ Non-equalization effect with larger standard deviation <๐‘„

๐‘— (๐‘Œ)>: Slowly-decreasing function โ†’ Weak equalization effect

with smaller standard deviation โ†’ ๐‘๐‘ก

(๐‘Œ): Flat in a wide region of intermediate and large ๐ธ

(strong equalization effect)

  • Strong Equalization Effect in ๐‘ต๐’•

(๐’€)

Cooperative interplay between the weak equalization effect in decreasing <๐‘„

๐‘— (๐‘Œ)> and the non-equalization effect in increasing <๐‘ƒ๐‘— (๐‘Œ)>

โ†’ Strong equalization effect in ๐‘๐‘ก

(๐‘Œ) with much smaller

standard deviation

Open circles: Interpopulation STDPs Solid circles: Absence of STDP Left: Absence of STDP Right: Interpopulation STDPs

slide-7
SLIDE 7

Summary

7

  • Fast Sparsely Synchronization (FSS) in the Absence of STDP
  • FSS (related to diverse cognitive functions) occurs in the clustered small-world networks

with two inhibitory and excitatory populations.

  • Effect of Interpopulation (I to E & E to I) STDPs on the FSS
  • Degree of good synchronization gets decreased, while degree of bad synchronization

becomes increased.

  • Degree of FSS becomes nearly the same in a wide range of noise intensity.

โ†’ Occurrence of Strong Equalization Effect

(also, occurrence of dumbing- down effect)

  • cf. Matthew effect in Intrapopulation (I to I & E to E) synaptic plasticity:

Good (bad) synchronization becomes better (worse).

[1] S.-Y. Kim & W. Lim, Neural Netw. 106, 50 (2018). [2] S.-Y. Kim & W. Lim, Neural Netw. 97, 92 (2018).