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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 1 , Dan O'Shea 2 , Werapong Goo 2 , Salvador Dura-Bernal 1 , Joe Francis 1 , Ilka Diester 2 , Paul Kalanithi 2 , Karl Deisseroth 2 , Krishna V . Shenoy 2 , William W . Lytton 1.

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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 Kerr1, Dan O'Shea2, WerapongGoo2, Salvador Dura-Bernal1, Joe Francis1, Ilka Diester2, Paul Kalanithi2, Karl Deisseroth2, Krishna V. Shenoy2, William W. Lytton1 • 1 SUNY Downstate 2 Stanford Universitywww.neurosimlab.org www.stanford.edu/~shenoy

  2. Outline • Methods • Optogenetics • Spiking network modeling • 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 & robotic arms

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

  7. Model dynamics

  8. Optogenetic response

  9. Optogenetic response

  10. Optogenetic response

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

  12. Granger causality Time series A Granger-causes Bif A’s past helps predict B:

  13. Granger causality • Stimulation reduces causality in mrhythm band (~10 Hz)

  14. Granger causality • Causality is induced at stimulation frequency (~40 Hz)

  15. 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

  16. Acknowledgements Werapong Goo(experiments) Joseph T. Francis(modeling) Paul Kalanithi(optogenetics) Krishna V. Shenoy(experiments) Daniel J. O'Shea(experiments) Salvador Dura-Bernal(modeling) Ilka Diester(optogenetics) Karl Deisseroth(optogenetics) William W. Lytton(modeling)

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