1 / 116

Lecture 11: Networks II: conductance-based synapses, visual cortical hypercolumn model

Lecture 11: Networks II: conductance-based synapses, visual cortical hypercolumn model. References: Hertz, Lerchner, Ahmadi, q-bio.NC/0402023 [Erice lectures] Lerchner, Ahmadi, Hertz, q-bio.NC/0402026 (Neurocomputing, 2004) [conductance-based synapses]

ayla
Download Presentation

Lecture 11: Networks II: conductance-based synapses, visual cortical hypercolumn model

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lecture 11: Networks II:conductance-based synapses, visual cortical hypercolumn model References: Hertz, Lerchner, Ahmadi, q-bio.NC/0402023 [Erice lectures] Lerchner, Ahmadi, Hertz, q-bio.NC/0402026 (Neurocomputing, 2004) [conductance-based synapses] Lerchner, Sterner, Hertz, Ahmadi, q-bio.NC/0403037 [orientation hypercolumn model]

  2. Conductance-based synapses In previous model:

  3. Conductance-based synapses In previous model: But a synapse is a channel with a (neurotransmitter-gated) conductance:

  4. Conductance-based synapses In previous model: But a synapse is a channel with a (neurotransmitter-gated) conductance:

  5. Conductance-based synapses In previous model: But a synapse is a channel with a (neurotransmitter-gated) conductance: where is the synaptically-filtered presynaptic spike train

  6. Conductance-based synapses In previous model: But a synapse is a channel with a (neurotransmitter-gated) conductance: where is the synaptically-filtered presynaptic spike train kernel:

  7. Conductance-based synapses In previous model: But a synapse is a channel with a (neurotransmitter-gated) conductance: where is the synaptically-filtered presynaptic spike train kernel:

  8. Conductance-based synapses In previous model: But a synapse is a channel with a (neurotransmitter-gated) conductance: where is the synaptically-filtered presynaptic spike train kernel:

  9. Model

  10. Model

  11. Model

  12. Mean field theory Effective single-neuron problem with synaptic input current

  13. Mean field theory Effective single-neuron problem with synaptic input current

  14. Mean field theory Effective single-neuron problem with synaptic input current with

  15. Mean field theory Effective single-neuron problem with synaptic input current with where = correlation function of synaptically-filtered presynaptic spike trains

  16. Balance condition Total mean current = 0:

  17. Balance condition Total mean current = 0:

  18. Balance condition Total mean current = 0: Mean membrane potential just below q:

  19. Balance condition Total mean current = 0: Mean membrane potential just below q: define

  20. Balance condition Total mean current = 0: Mean membrane potential just below q: define

  21. Balance condition Total mean current = 0: Mean membrane potential just below q: define Solve for rb as in current-based case:

  22. Balance condition Total mean current = 0: Mean membrane potential just below q: define Solve for rb as in current-based case:

  23. Balance condition Total mean current = 0: Mean membrane potential just below q: define Solve for rb as in current-based case: =>

  24. High-conductance-state

  25. High-conductance-state

  26. High-conductance-state Va “chases” Vsa(t) at rate gtot(t)

  27. High-conductance-state Va “chases” Vsa(t) at rate gtot(t)

  28. High-conductance-state Va “chases” Vsa(t) at rate gtot(t)

  29. High-conductance-state Va “chases” Vsa(t) at rate gtot(t) Effective membrane time constant ~ 1 ms

  30. Membrane potential and spiking dynamics for large gtot

  31. Fluctuations Measure membrane potential from :

  32. Fluctuations Measure membrane potential from :

  33. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations:

  34. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations:

  35. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations:

  36. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations: Use balance equation in

  37. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations: Use balance equation in =>

  38. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations: Use balance equation in => or

  39. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations: Use balance equation in => or with

  40. Fluctuations Measure membrane potential from : Conductances: mean + fluctuations: Use balance equation in => or with

  41. Effective current-based model High connectivity:

  42. Effective current-based model High connectivity:

  43. Effective current-based model High connectivity:

  44. Effective current-based model High connectivity:

  45. Effective current-based model High connectivity:

  46. Effective current-based model High connectivity: Like current-based model with

  47. Effective current-based model High connectivity: Like current-based model with (but effective membrane time constant depends on presynaptic rates)

  48. Firing irregularity depends on reset level and ts

  49. Modeling primary visual cortex

  50. Modeling primary visual cortex Background: • Neurons in primary visual cortex (area V1) respond strongly to oriented • stimuli (bars, gratings)

More Related