1. Final Projects
The final project is expected to be an independent project of your own devising addressing a topic related to cognition or neuroscience. The project proposal should be about 1 page long and should be formulated after discussion with Jay or Steven. The final project report should describe the background and rationale for your project, the details of what you did and why you did it, your results and related analysis and discussion. These reports should be about 3,000-4,000 words plus figures, tables, and references. More details will be provided. The Project will count for 40% of the overall grade in the class.
Proposal Due: Tuesday, Feb 28, before midnight – assignment will be posted.
In class presentations: Week 10
Paper Due, Wed of Finals Week

2. Ideal projects – KISS!
Address a topic in cognition, perception, action/control, development that interests you
Attempt to capture some aspect of human abilities, either at the level of behavior or neural data/phenomena
Are motivated by a statement of purpose and a literature review
Should be as simple as possible while addressing an aspect of the phenomenon of interest.
Are unlikely to be publishable finished projects but may be first steps toward the same
Should involve a neural network model or work that relates in some way to neural network models
Should involve analysis as well as results.
Can be partnerships but proposals and writups must be independent and should involve different emphases

3. Immediate task: Proposal due Tues Feb 28
The project proposal should be about 1 page long and should be formulated after discussion with Jay or Steven.
Make an appointment! Saturday times available!
Come with a topic – leave with a plan
Paired meetings welcome!

4. Project paper outline and desiderata
Introduction: Background and rationale for your project highlighting innovative aspects of methods and analysis
Methods: The details of what you did and why you did it Replicability standard
Results and analyses Dig as deeply as you can to understand why you found what you did
Discussion
Success is unimportant: Insight is what counts!

5. Yamins et al (2014), Performance optimized hierarchical models predict neural responses
Psych 209 – 2017
February 21, 2017

6. Orientation questions
What is the architecture of the network like?  What properties does it have and do they seem reasonable in light of what the authors say about the organization of the brain?  If you have additional knowledge of the brain, do you agree with their characterization of this organization?
What is the model optimized for?  Consider not only the task, but the training set.  Is it interesting that it fits data from neurons in IT cortex, given that it is not optimized to fit these neuronal responses per se?  Why or why not?
How is the model's performance compared to neurons in IT cortex?  Do you agree or disagree with how these comparisons were done?   What, do you think, is the right way to compare the network's representations to the representations observed in recordings from neurons?
What extensions of this work do you think might be interesting?  Consider the task the architecture, the quesitons you might ask about representations...

7. Model Overview

8. Testing and screening data

9. The model

10. Diversity and specificity

11. Performance vs IT Variance Explained

13. Representational Similarity Analysis

14. V4 predictions