"A call to arms" for Computational Cognitive Neuroscience.

1. Dr. Brian J. Spiering
email: bspiering@gmail.com
website: brianspiering.com
The Theory & Practice
of
Computational Cognitive
Neuroscience

6. Oblate Spheroid

7. Oblate Spheroid
with unequal mass distribution

8. Choose the model
based on the task

9. Overview
I) Context
II) Theory
III) Practice

10. 1) Context
A) How to Buy a Science model
B) Validating Models
C) Hierarchy of Models
D) The First Step

11. What to look for when
“buying” a science model ...
1) Accounts for previously accounted for data
2) Accounts for previously unaccounted for data
3) Makes novel, falsifiable predictions

12. Validating Models
It is easy to account for behavioral data
so add constraints ...
- wider (more tasks)
- deeper (more methodologies)
- neuroscience

13. Hierarchy of Models
No Model
Modules
Circles & Lines
CCN

14. Why is CCN on the top?
Can predict in these domains:
- Behavioral
- fMRI
- MEG & EEG
- Single-unit recording

15. Why is CCN on the top?
Can predict account for these factors:
- Experimental Manipulation
- Drugs
- Disease
- Genes
- Focal Lesions (injury, ablation, & TMS)

16. Where do you start?
Model ➜ Brain
or
Brain ➜ Model

17. II) Theory of Modeling
A) Ideals
B) Modeling individual units
C) Learning
D) Behavior
E) Other Issues

18. A) Ideals
1) Neuroscience
2) Simplicity
3) Set-in-Stone
4) Goodness-of-Fit

19. 1) Neuroscience
a) Use only found connections
b) Specify excitatory or inhibitory
c) Account for qualitative behavior in units
d) Agree with neuroscience of learning

20. 2) Simplicity
Use enough but not any more

21. 3) Set-in-Stone
Always use the same players and play by
the same rules in the modeling game.

22. 4) Goodness-of-Fit
a) Behavioral
b) Neuroscience

23. B) Modeling Units
1) Leaky integrate-and-fire model
2) Izhikevich model
3) Axon & synaptic delays

24. C) Learning
1) LTP vs. LTD
2) Role of Dopamine
3) Discrete vs. Continuous
4) Local vs. Global

25. D) Behavior
1) Choose control region
2) Choose function

26. E) Other Issues
1) Overconnectioning
2) Other?

27. III) Practice of Modeling
SPEED Model:
A) Idea
B) Inception
C) Instantiations

28. A) SPEED Model Idea
An Contrarian Model of Procedural Expertise
F. Greg Ashby John Ennis

29. SPEED
An Contrarian Model of Procedural Expertise

30. Previous Notions
“It has been widely held that although memory
traces are at first formed in the cerebral cortex,
they are finally reduced or transferred by long
practice to subcortical levels” (p. 466)
Karl Lashley (1950)
In search of the engram.

31. Previous Notions
“Routine, automatic, or overlearned behavioral
sequences, however complex, do not engage the
PFC and may be entirely organized in subcortical
structures” (p. 323)
Joaquin Fuster (2001).
The prefrontal cortex – an update.

32. SPEED
An Contrarian Model of Procedural Expertise

33. SPEED Model
Procedural learning is striatum dependent.
Procedural expertise is striatum independent.
SPEED (Subcortical Pathways Enable Expertise Development)

34. B) Inception
aka, How we built the model.

35. Sensory
Association
Cortex
Response
BA
B
A-B difference
A
B
A B
A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:

36. Sensory
AssociationA Response
A
A
A
A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
Excitatory
Inhibitory
SPEED Model:
Single Subcortical

37. Sensory
Association
Cortex
Response
BA
B
A-B difference
A
B
A B
A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:
Subcortical

38. Sensory
Association
Cortex
Striatum
SNpc
Dopamine
Excitatory
SPEED Model:
Subcortical Learning

40. Sensory
Association
Cortex
Response
BA
B
A-B difference
A
B
A B
A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:
Subcortical

41. Sensory
Association
Cortex
Response
BA
A-B difference
SPEED Model:
Cortical

42. Sensory
Association
Cortex
Response
BA
B
A-B difference
A
B
A B
A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:
Cortical Learning

43. Sensory
Association
Cortex
A
A
A A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:
1st Trial

44. Sensory
Association
Cortex
A
A
A A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:
Early Learning

45. Sensory
Association
Cortex
A
A
A A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:
Middle Learning

46. Sensory
Association
Cortex
A
A
A A
Premotor Area
(Cortex)
Thalamus
Globus Pallidus
Striatum
SPEED Model:
Mature Performance

47. C) Instantiations
See MATLAB code ...

48. Summary
1) Context
II) Theory
III) Practice

49. ?