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~~DISCUSSION~~ | ~~DISCUSSION~~ | ||
- | Welcome to the %%CoSMo%% 2014 hands-on session on decoding neural ensemble data! | + | **Welcome to the %%CoSMo%% 2014 hands-on session on decoding neural ensemble data!** |
The Tutorial modules below constitute a step-by-step walkthrough that introduces you to a data set of 100+ neurons, recorded simultaneously from hippocampal subfield CA1 as a rat runs a T-maze task, followed by some example decoding analyses. | The Tutorial modules below constitute a step-by-step walkthrough that introduces you to a data set of 100+ neurons, recorded simultaneously from hippocampal subfield CA1 as a rat runs a T-maze task, followed by some example decoding analyses. | ||
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If you finish the tutorial part, there are two suggested "mini-projects" that indicate directions for cutting-edge analyses you can pursue in a more open-ended way, building on the material in the tutorial but requiring more active coding and problem-solving on your part. | If you finish the tutorial part, there are two suggested "mini-projects" that indicate directions for cutting-edge analyses you can pursue in a more open-ended way, building on the material in the tutorial but requiring more active coding and problem-solving on your part. | ||
- | Finally, this is a wiki: if you see an opportunity for improvement, go ahead and edit it. These pages will stay available for at least a number of months after the course. | + | Finally, this is a wiki: if you see an opportunity for improvement, go ahead and edit it. These pages will stay available for at least a number of months after the course. If you'd rather not edit but just comment, there is a discussion/comment box at the bottom of each page. |
=== Tutorial === | === Tutorial === | ||
- | * [[analysis:cosmo2014:module1|Module 1: Setting up the code, data, and MATLAB paths (good data analysis habits; 15 minutes)]] | + | * [[analysis:cosmo2014:module1|Module 1: Setting up the code, data, and MATLAB paths (10 minutes)]] |
- | * [[analysis:cosmo2014:module2|Module 2: Data types, loading, and visualization (place fields, phase precession; 30 minutes)]] | + | * [[analysis:cosmo2014:module2|Module 2: Data types, loading, and visualization (20 minutes)]] |
- | * [[analysis:cosmo2014:module3|Module 3: Decoding (45 minutes)]] | + | * [[analysis:cosmo2014:module3|Module 3: Basic "one-step" decoding (30 minutes)]] |
+ | * [[analysis:cosmo2014:module4|Module 4: "Two-step" decoding with dynamic spatial prior (30 minutes)]] | ||
+ | * [[analysis:cosmo2014:module5|Module 5: Decoding cognitive processes (30 minutes)]] | ||
+ | (Times indicated are suggestions of the //maximum// time you should spend on each part. If everything is clear you can of course move on earlier than that!) | ||
=== Mini-projects === | === Mini-projects === | ||
+ | * [[http://www.sciencedirect.com/science/article/pii/S0896627314003420|Bieri et al. (2014)]] found that theta cycles with slow-gamma power (thought to reflect inputs from CA3 to CA1) were associated with sequences more forward of the animal compared to those with fast-gamma power (thought to reflect inputs from EC to CA1). This seems counterintuitive if we assume that each gamma cycle is associated with an "item" in the sequence (e.g. [[http://www.cell.com/neuron/abstract/S0896-6273(13)00231-6|Lisman and Jensen, 2013]]): in that case, slow-gamma should have //fewer//, not more, items per sequence! Can we resolve this apparent discrepancy by quantifying the speed of the sequences during slow- and fast-gamma associated theta cycles? | ||
+ | * This particular data set was recorded when the animal was mildly water-deprived. The maze had food on the end of one arm, and water on the other arm; a prediction of the theory that hippocampal replay reflects a goal-directed planning process is that the content of this process should be influenced by motivational state. That is, a hungry animal may "replay" food-associated trajectories more than water-associated trajectories, and vice versa. Can we test this idea by mapping the content of replays onto the maze? | ||
=== Reference === | === Reference === | ||