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Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models Ruoxi Sun*, Scott Linderman*, Ian Kinsella, Liam Paninski Columbia University NeurIPS 2019 Dendritic voltage imaging Hochbaum et al Nature Methods,

  1. Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models Ruoxi Sun*, Scott Linderman*, Ian Kinsella, Liam Paninski Columbia University NeurIPS 2019

  2. Dendritic voltage imaging Hochbaum et al Nature Methods, 2014

  3. Multiple Compartment models https://biology.stackexchange.com/questions/44082/can-the-dendrites-of-sensory-neurons-be-a-meter-long

  4. Multiple Compartment models https://biology.stackexchange.com/questions/44082/can-the-dendrites-of-sensory-neurons-be-a-meter-long

  5. Multiple Compartment models Compartment 1 1 2 3 4 2 3 4 https://biology.stackexchange.com/questions/44082/can-the-dendrites-of-sensory-neurons-be-a-meter-long

  6. Biophysics Cable equation theory g : conductance; I : current; R : resistance; V : voltage; C : capacitance Compartment n:

  7. Biophysics to Statistics Model

  8. Model Single Compartment Dynamics one time step theta: parameters Z: discrete latent variable X: continuous latent variable (cycle parameters) V: continuous latent variable (denoised voltage) Y: observed variables

  9. Model Single Compartment Dynamics Recurrent Switching Linear Dynamical System (rSLDs) ●

  10. Statistical Model Recurrent Switching Linear Dynamical System (rSLDs) ● Physical model theta : parameters; Z : discrete latent variable; X : continuous latent variable (cycle parameters); V : continuous latent variable (denoised voltage); Y : observed variables

  11. Statistical Model Recurrent Switching Linear Dynamical System (rSLDs) ● theta : parameters; Z : discrete latent variable; X : continuous latent variable (cycle parameters); V : continuous latent variable (denoised voltage); Y : observed variables Linderman et al (AISTATS 2017)

  12. Model Inter-Compartment Dynamics

  13. Linear Dependency between Adjacent Compartments

  14. Results: Single Compartment

  15. Output of the model for Single Compartment model ● Observed Voltage (y) ● Inferred Continuous Latent State: V (voltage) and X (cycle) V

  16. ● Inferred Discrete Latent State (Z)

  17. ● Generated new spike (voltage)

  18. Results: Multiple Compartments

  19. Multiple Compartment denoising

  20. Inferred Voltage

  21. Thank you! Poster: #147 Code: https://github.com/SunRuoxi/Voltage_Smoothing_with_rSLDS

  22. Previous Biophysical work ● Hodgkin Huxley ● Fitzhugh-Nagumo

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