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

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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  1. 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

  2. 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?

  3. 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

  4. 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

  5. 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

  6. Spiking network model • Connectivity matrix based on rat, cat, and macaque data • Strong connectivity within each layer

  7. Model dynamics

  8. Optogeneticresponse 1

  9. Optogeneticresponse 2

  10. Network-level effects Simulation Experiment Response falls off as 1/r2 from optrode Connectivity can explain firing rate heterogeneity

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

  12. Granger causality: results • Stimulation reduces causality in mu rhythm band (~10 Hz)

  13. 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

  14. 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)

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