Building better models in cognitive neuroscience. Part 1: Theory

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Describes a rigorous, proven method to create quantitate neuroscience theories.

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Building better models in cognitive neuroscience. Part 1: Theory

  1. 1. Dr. Brian J. Spiering Building Better Models in Cognitive Neuroscience: THEORY
  2. 2. Better models help solve better problems
  3. 3. How to Build Better A) Why? B) Ideals C) Modeling Cognitive Neuroscience Models
  4. 4. Increased Constraints
  5. 5. Find Unexpected Relationships
  6. 6. Make Novel Predictions
  7. 7. Better Model Testing
  8. 8. IDEALS
  9. 9. Neuroscience
  10. 10. 1) Be consistent only known connections 2) Excitatory or Inhibitory Connections: Pick One 3) Predicted behavior consistent with known behavior 4) Predicted learning consistent with known learning
  11. 11. Simplicity
  12. 12. Set-in-Stone
  13. 13. Input Black Box Output
  14. 14. Goodness-of-Fit
  15. 15. Biological Simplicity vs. Scalability
  16. 16. Units Connections Learning Behavior
  17. 17. Units
  18. 18. MODELING UNITS
  19. 19. Leaky Integrate-and-fire Model
  20. 20. Leaky Integrate-and-fire Model
  21. 21. Leaky Integrate-and-fire Model
  22. 22. Activation
  23. 23. Set spiking threshold,Vpeak, onVB(t)
  24. 24. Leaky Integrate-and-fire Model with quadratic polynomial
  25. 25. Izhikevich model
  26. 26. See Figure 3 in Paper
  27. 27. Units Connections
  28. 28. Axon & Synaptic Delays
  29. 29. Break
  30. 30. L E A R N I N G
  31. 31. Units Connections Learning
  32. 32. Long-Term Potentiation (LTP) Long lasting increase in the efficacy of a synapse
  33. 33. Long-Term Depression (LTD) Long lasting decrease in the efficacy of a synapse
  34. 34. Dopamine Important function but depends on location
  35. 35. Discrete-Time vs. Continuous-Time
  36. 36. Discrete Time & Slow DA Reuptake
  37. 37. Discrete Time & Slow DA Reuptake
  38. 38. Discrete Time & Fast DA Reuptake
  39. 39. Modeling Dopamine Release
  40. 40. Input Black Box Output
  41. 41. Predicted Reward - Reward Prediction Error (RPE) Obtained Reward =
  42. 42. Obtained Reward (Simple) +1 if correct -1 if error
  43. 43. Predicted Reward (Simple)
  44. 44. Dopamine & Reward Prediction Error
  45. 45. Continuous-time models of learning
  46. 46. Learning Rules: Local vs. Global
  47. 47. +
  48. 48. BEHAVIOR
  49. 49. Units Connections Learning Behavior
  50. 50. 1) Which brain regions controls behavior?
  51. 51. Input Black Box Output
  52. 52. 2) What is the neural activity function that drives the decision?
  53. 53. 1)Integrated neural activity 2)Spiking behavior 3)Integrated output alpha function
  54. 54. Integrated Output Alpha Function
  55. 55. 3) How is response competition resolved?
  56. 56. Two Choice Tasks 1)Diffusion Model 2)Accumulator/Race Model with Lateral Inhibition
  57. 57. >Two Choice Tasks • Set criterion at each decision unit • 1st unit that crosses threshold “wins” • Add lateral inhibition
  58. 58. OVERCONNECTING
  59. 59. Have a Happy Thanksgiving!

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