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analysis:cosmo2014 [2014/08/13 11:31]
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analysis:cosmo2014 [2018/07/07 10:19] (current)
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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]] +  * [[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 (20 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 ===
  
analysis/cosmo2014.1407943879.txt.gz · Last modified: 2018/07/07 10:19 (external edit)