This document discusses building better models in cognitive neuroscience. It outlines several key ideals for models, including being consistent with known neuroscience and keeping models as simple as possible while still capturing important behaviors and learning mechanisms. The document then covers different approaches to modeling units, connections, and learning in neural networks. Specific models discussed include leaky integrate-and-fire models and the Izhikevich model for units, as well as long-term potentiation and depression for learning. The document concludes by discussing how to model behavior and decision making using neural network outputs and competition between choices.

1. Dr. Brian J. Spiering
Building Better Models in
Cognitive Neuroscience:
THEORY

5. Better models
help solve
better problems

6. How to Build Better
A) Why?
B) Ideals
C) Modeling
Cognitive Neuroscience
Models

8. Increased
Constraints

9. Find Unexpected
Relationships

10. Make Novel
Predictions

11. Better Model
Testing

12. IDEALS

13. Neuroscience

14. 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

15. Simplicity

16. Set-in-Stone

17. Input
Black Box
Output

18. Goodness-of-Fit

19. Biological Simplicity
vs.
Scalability

20. Units
Connections
Learning
Behavior

21. Units

22. MODELING
UNITS

23. Leaky Integrate-and-fire Model

24. Leaky Integrate-and-fire Model

25. Leaky Integrate-and-fire Model

26. Activation

27. Set spiking threshold,Vpeak,
onVB(t)

29. Leaky Integrate-and-fire Model
with quadratic polynomial

31. Izhikevich model

32. See Figure 3 in Paper

33. Units
Connections

34. Axon & Synaptic Delays

35. Break

36. L
E
A
R
N
I
N
G

37. Units
Connections
Learning

38. Long-Term Potentiation
(LTP)
Long lasting increase in
the efficacy of a synapse

39. Long-Term Depression
(LTD)
Long lasting decrease in
the efficacy of a synapse

40. Dopamine
Important function
but depends on location

43. Discrete-Time
vs.
Continuous-Time

44. Discrete Time & Slow DA Reuptake

45. Discrete Time & Slow DA Reuptake

46. Discrete Time & Fast DA Reuptake

47. Modeling Dopamine Release

48. Input
Black Box
Output

49. Predicted
Reward
-
Reward
Prediction
Error
(RPE)
Obtained
Reward
=

50. Obtained Reward (Simple)
+1 if correct
-1 if error

51. Predicted Reward (Simple)

52. Dopamine &
Reward Prediction Error

53. Continuous-time models of learning

54. Learning Rules:
Local
vs.
Global

57. +

58. BEHAVIOR

59. Units
Connections
Learning
Behavior

60. 1) Which brain regions
controls behavior?

61. Input
Black Box
Output

62. 2) What is the neural activity
function that drives the decision?

63. 1)Integrated neural activity
2)Spiking behavior
3)Integrated output alpha function

64. Integrated Output Alpha Function

65. 3) How is response
competition resolved?

66. Two Choice Tasks
1)Diffusion Model
2)Accumulator/Race Model with
Lateral Inhibition

67. >Two Choice Tasks
• Set criterion at each decision unit
• 1st unit that crosses threshold “wins”
• Add lateral inhibition

68. OVERCONNECTING

69. Have a Happy
Thanksgiving!