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Network-level e ffects of optogenetic s timulation : experiment and simulation. Cliff Kerr, Dan O'Shea, Werapong Goo, Salvador Dura-Bernal, Joe Francis, Ilka Diester , Paul Kalanithi , Karl Deisseroth , Krishna V. Shenoy , William W. Lytton.

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Network-level e ffects of optogenetic s timulation : experiment and simulation


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network level e ffects of optogenetic s timulation experiment and simulation

Network-level effects of optogenetic stimulation: experiment and simulation

Cliff Kerr, Dan O'Shea, WerapongGoo, Salvador Dura-Bernal, Joe Francis, Ilka Diester, Paul Kalanithi, Karl Deisseroth, Krishna V. Shenoy, William W. Lytton

  • Neurosimulation Laboratory, SUNY Downstate Medical Centerwww.neurosimlab.org
outline
Outline
  • Methods
    • How does optogenetics work?
    • How does a spiking network model work?
  • Results
    • How does optogenetic stimulation influence network actvity – and vice versa?
    • How does optogenetic stimulation influence information flow?
optogenetics
Optogenetics

New York Times, 2011

Viral insertion of channelrhodopsin

Neuronal activation and recording via optrode (electrode + optical fiber)

Wang et al., IEEE 2011

Adamantidis et al., Nature 2007

spiking network model
Spiking network model
  • 6-layered cortex
  • Izhikevich(integrate-and-fire) neurons
  • 4 types of neuron: regular or bursting (excitatory), fast or low-threshold (inhibitory)
  • 24,800 neurons total

Kerr et al.,

Frontiers 2014

spiking network model1
Spiking network model

Chadderdon et al., PLOS ONE 2012

Neural equations:

Anatomy & physiology based on experimental data

Generates realistic dynamics

Adaptable to different brain regions (e.g. sensory, motor)

Demonstrated control of virtual arm

spiking network model2
Spiking network model
  • Connectivity matrix based on rat, cat, and macaque data
  • Strong connectivity within each layer
network level effects
Network-level effects

Simulation

Experiment

Response falls off as 1/r2 from optrode

Connectivity can explain firing rate heterogeneity

granger causality primer
Granger causality: primer

Time series A Granger-causes B if knowledge of A’s past helps predict B:

granger causality results
Granger causality: results
  • Stimulation reduces causality in mu rhythm band (~10 Hz)
summary
Summary

First network model of optogenetics

Synaptic connections determine the network’s response to optogeneticstimulation

Optogeneticstimulation may be used to modulate information flow

Future work: predicting the effects of specific stimulation protocols

acknowledgements
Acknowledgements

Werapong Goo(experiments)

Joseph T. Francis(modeling)

Paul Kalanithi(optogenetics)

Krishna V. Shenoy(optogenetics)

Daniel J. O'Shea(experiments)

Salvador Dura-Bernal(modeling)

Ilka Diester(optogenetics)

Karl Deisseroth(optogenetics)

William W. Lytton(modeling)