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The theory and practice of computational cognitive neuroscience

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"A call to arms" for Computational Cognitive Neuroscience.

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The theory and practice of computational cognitive neuroscience

  1. 1. Dr. Brian J. Spiering email: bspiering@gmail.com website: brianspiering.com The Theory & Practice of Computational Cognitive Neuroscience
  2. 2. Oblate Spheroid
  3. 3. Oblate Spheroid with unequal mass distribution
  4. 4. Choose the model based on the task
  5. 5. Overview I) Context II) Theory III) Practice
  6. 6. 1) Context A) How to Buy a Science model B) Validating Models C) Hierarchy of Models D) The First Step
  7. 7. 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
  8. 8. Validating Models It is easy to account for behavioral data so add constraints ... - wider (more tasks) - deeper (more methodologies) - neuroscience
  9. 9. Hierarchy of Models No Model Modules Circles & Lines CCN
  10. 10. Why is CCN on the top? Can predict in these domains: - Behavioral - fMRI - MEG & EEG - Single-unit recording
  11. 11. Why is CCN on the top? Can predict account for these factors: - Experimental Manipulation - Drugs - Disease - Genes - Focal Lesions (injury, ablation, & TMS)
  12. 12. Where do you start? Model ➜ Brain or Brain ➜ Model
  13. 13. II) Theory of Modeling A) Ideals B) Modeling individual units C) Learning D) Behavior E) Other Issues
  14. 14. A) Ideals 1) Neuroscience 2) Simplicity 3) Set-in-Stone 4) Goodness-of-Fit
  15. 15. 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
  16. 16. 2) Simplicity Use enough but not any more
  17. 17. 3) Set-in-Stone Always use the same players and play by the same rules in the modeling game.
  18. 18. 4) Goodness-of-Fit a) Behavioral b) Neuroscience
  19. 19. B) Modeling Units 1) Leaky integrate-and-fire model 2) Izhikevich model 3) Axon & synaptic delays
  20. 20. C) Learning 1) LTP vs. LTD 2) Role of Dopamine 3) Discrete vs. Continuous 4) Local vs. Global
  21. 21. D) Behavior 1) Choose control region 2) Choose function
  22. 22. E) Other Issues 1) Overconnectioning 2) Other?
  23. 23. III) Practice of Modeling SPEED Model: A) Idea B) Inception C) Instantiations
  24. 24. A) SPEED Model Idea An Contrarian Model of Procedural Expertise F. Greg Ashby John Ennis
  25. 25. SPEED An Contrarian Model of Procedural Expertise
  26. 26. 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.
  27. 27. 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.
  28. 28. SPEED An Contrarian Model of Procedural Expertise
  29. 29. SPEED Model Procedural learning is striatum dependent. Procedural expertise is striatum independent. SPEED (Subcortical Pathways Enable Expertise Development)
  30. 30. B) Inception aka, How we built the model.
  31. 31. Sensory Association Cortex Response BA B A-B difference A B A B A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model:
  32. 32. Sensory AssociationA Response A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum Excitatory Inhibitory SPEED Model: Single Subcortical
  33. 33. Sensory Association Cortex Response BA B A-B difference A B A B A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Subcortical
  34. 34. Sensory Association Cortex Striatum SNpc Dopamine Excitatory SPEED Model: Subcortical Learning
  35. 35. Sensory Association Cortex Response BA B A-B difference A B A B A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Subcortical
  36. 36. Sensory Association Cortex Response BA A-B difference SPEED Model: Cortical
  37. 37. 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
  38. 38. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: 1st Trial
  39. 39. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Early Learning
  40. 40. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Middle Learning
  41. 41. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Mature Performance
  42. 42. C) Instantiations See MATLAB code ...
  43. 43. Summary 1) Context II) Theory III) Practice
  44. 44. ?

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