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Building better models in cognitive neuroscience. Part 1: Theory
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Building better models in cognitive neuroscience. Part 1: Theory

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

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

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