The theory and practice of computational cognitive neuroscience

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"A call to arms" for Computational Cognitive Neuroscience.

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<ul><li> 1. Dr. Brian J. Spiering email: bspiering@gmail.com website: brianspiering.com The Theory &amp; Practice of Computational Cognitive Neuroscience </li></ul> <p> 2. Oblate Spheroid 3. Oblate Spheroid with unequal mass distribution 4. Choose the model based on the task 5. Overview I) Context II) Theory III) Practice 6. 1) Context A) How to Buy a Science model B) Validating Models C) Hierarchy of Models D) The First Step 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, falsiable predictions 8. Validating Models It is easy to account for behavioral data so add constraints ... - wider (more tasks) - deeper (more methodologies) - neuroscience 9. Hierarchy of Models No Model Modules Circles &amp; Lines CCN 10. Why is CCN on the top? Can predict in these domains: - Behavioral - fMRI - MEG &amp; EEG - Single-unit recording 11. Why is CCN on the top? Can predict account for these factors: - Experimental Manipulation - Drugs - Disease - Genes - Focal Lesions (injury, ablation, &amp; TMS) 12. Where do you start? Model Brain or Brain Model 13. II) Theory of Modeling A) Ideals B) Modeling individual units C) Learning D) Behavior E) Other Issues 14. A) Ideals 1) Neuroscience 2) Simplicity 3) Set-in-Stone 4) Goodness-of-Fit 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. 2) Simplicity Use enough but not any more 17. 3) Set-in-Stone Always use the same players and play by the same rules in the modeling game. 18. 4) Goodness-of-Fit a) Behavioral b) Neuroscience 19. B) Modeling Units 1) Leaky integrate-and-re model 2) Izhikevich model 3) Axon &amp; synaptic delays 20. C) Learning 1) LTP vs. LTD 2) Role of Dopamine 3) Discrete vs. Continuous 4) Local vs. Global 21. D) Behavior 1) Choose control region 2) Choose function 22. E) Other Issues 1) Overconnectioning 2) Other? 23. III) Practice of Modeling SPEED Model: A) Idea B) Inception C) Instantiations 24. A) SPEED Model Idea An Contrarian Model of Procedural Expertise F. Greg Ashby John Ennis 25. SPEED An Contrarian Model of Procedural Expertise 26. Previous Notions It has been widely held that although memory traces are at rst formed in the cerebral cortex, they are nally reduced or transferred by long practice to subcortical levels (p. 466) Karl Lashley (1950) In search of the engram. 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. SPEED An Contrarian Model of Procedural Expertise 29. SPEED Model Procedural learning is striatum dependent. Procedural expertise is striatum independent. SPEED (Subcortical Pathways Enable Expertise Development) 30. B) Inception aka, How we built the model. 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. Sensory AssociationA Response A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum Excitatory Inhibitory SPEED Model: Single Subcortical 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. Sensory Association Cortex Striatum SNpc Dopamine Excitatory SPEED Model: Subcortical Learning 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. Sensory Association Cortex Response BA A-B difference SPEED Model: Cortical 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. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: 1st Trial 39. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Early Learning 40. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Middle Learning 41. Sensory Association Cortex A A A A Premotor Area (Cortex) Thalamus Globus Pallidus Striatum SPEED Model: Mature Performance 42. C) Instantiations See MATLAB code ... 43. Summary 1) Context II) Theory III) Practice 44. ? </p>