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analysis:course-w16:week10 [2016/02/14 15:21]
mvdm
analysis:course-w16:week10 [2016/02/16 14:01]
mvdm [Quantifying decoding accuracy]
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 ~~DISCUSSION~~ ~~DISCUSSION~~
- 
-:!: **UNDER CONSTRUCTION -- PLEASE DO NOT USE YET** :!: 
  
 ===== Spike train analysis II: tuning curves, encoding, decoding ===== ===== Spike train analysis II: tuning curves, encoding, decoding =====
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   * Learn to estimate and plot tuning curves, raw and smoothed   * Learn to estimate and plot tuning curves, raw and smoothed
   * Implement a basic Bayesian decoding algorithm   * Implement a basic Bayesian decoding algorithm
-  * Compare decoded and actual position by exporting to a movie file+  * Compare decoded and actual position by computing the decoding error
  
 Resources: Resources:
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 <code matlab> <code matlab>
-ENC_S = restrict(S,run_start,​run_end); +LoadMetadata;​ 
-ENC_pos = restrict(pos,​run_start,​run_end); +ENC_S = restrict(S,metadata.taskvars.trial_iv); 
 +ENC_pos = restrict(pos,​metadata.taskvars.trial_iv); 
 + 
 % check for empties and remove % check for empties and remove
 keep = ~cellfun(@isempty,​ENC_S.t);​ keep = ~cellfun(@isempty,​ENC_S.t);​
 ENC_S.t = ENC_S.t(keep);​ ENC_S.t = ENC_S.t(keep);​
 ENC_S.label = ENC_S.label(keep);​ ENC_S.label = ENC_S.label(keep);​
 + 
 S.t = S.t(keep); S.t = S.t(keep);
 S.label = S.label(keep);​ S.label = S.label(keep);​
 </​code>​ </​code>​
  
-We have created ''​ENC_''​ versions of our spike trains and position data, containing only data from when the rat was running on the track (the ''​run_start''​ and ''​run_end''​ variables have been previously generated by a different script) and removed all cells from the data set that did not have any spikes on the track.+We have created ''​ENC_''​ versions of our spike trains and position data, containing only data from when the rat was running on the track (using experimenter annotation stored in the metadata; ​''​trial_iv'' ​contains the start and end times of trials) and removed all cells from the data set that did not have any spikes on the track.
  
 ☛ Plot the above scatterfield again for the restricted spike train. Verify that no spikes are occurring off the track by comparing your plot to the previous one for the full spike trains, above. ☛ Plot the above scatterfield again for the restricted spike train. Verify that no spikes are occurring off the track by comparing your plot to the previous one for the full spike trains, above.
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 y_edges = SET_ymin:​SET_yBinSz:​SET_ymax;​ y_edges = SET_ymin:​SET_yBinSz:​SET_ymax;​
  
-occ_hist = histcn(pos_mat,​y_edges,​x_edges);​+occ_hist = histcn(pos_mat,​y_edges,​x_edges); ​% 2-D version of histc()
  
-no_occ_idx = find(occ_hist == 0); % NaN out bins rat never visited+no_occ_idx = find(occ_hist == 0); % NaN out bins never visited
 occ_hist(no_occ_idx) = NaN; occ_hist(no_occ_idx) = NaN;
  
-occ_hist = occ_hist .* (1/30); % convert to seconds using video frame rate+occ_hist = occ_hist .* (1/30); % convert ​samples ​to seconds using video frame rate (30 Hz)
  
 subplot(221);​ subplot(221);​
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 {{ :​analysis:​course-w16:​raw_tc.png?​nolink&​900 |}} {{ :​analysis:​course-w16:​raw_tc.png?​nolink&​900 |}}
  
-Note that from the occupancy map, you can see the rat spent relatively more time at the choice point compared to other segments of the track. However, the rough binning is not very satisfying. Let's see if we can do better with some smoothing:+Note that from the occupancy map, you can see the rat spent relatively more time at the base of the stem compared to other segments of the track. However, the rough binning is not very satisfying. Let's see if we can do better with some smoothing:
  
 <code matlab> <code matlab>
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 occ_hist(no_occ_idx) = NaN; occ_hist(no_occ_idx) = NaN;
-occ_hist = occ_hist .* (1/​30); ​% convert to seconds using video frame rate+occ_hist = occ_hist .* (1/30);
  
 subplot(221);​ subplot(221);​
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 % %
 spk_hist = histcn(spk_mat,​y_edges,​x_edges);​ spk_hist = histcn(spk_mat,​y_edges,​x_edges);​
-spk_hist = conv2(spk_hist,​kernel,'​same'​);​+spk_hist = conv2(spk_hist,​kernel,'​same'​); ​% 2-D convolution
 spk_hist(no_occ_idx) = NaN; spk_hist(no_occ_idx) = NaN;
  
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 % assemble tvecs % assemble tvecs
-tvec_edges = run_start(1):​binsize:​run_end(end);+tvec_edges = metadata.taskvars.trial_iv.tstart(1):​binsize:​metadata.taskvars.trial_iv.tend(end);
 Q_tvec_centers = tvec_edges(1:​end-1)+binsize/​2;​ Q_tvec_centers = tvec_edges(1:​end-1)+binsize/​2;​
  
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 %% only include data we care about (runs on the maze) %% only include data we care about (runs on the maze)
 Q_tsd = tsd(Q_tvec_centers,​Q);​ Q_tsd = tsd(Q_tvec_centers,​Q);​
-Q_tsd = restrict(Q_tsd,​run_start,​run_end);+Q_tsd = restrict(Q_tsd,​metadata.taskvars.trial_iv);
 </​code>​ </​code>​
  
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 end end
-plot(Q_tvec_centers,​dec_err,'​.k'​);​ 
 </​code>​ </​code>​
  
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 % get trial id for each sample % get trial id for each sample
 trial_id = zeros(size(Q_tvec_centers));​ trial_id = zeros(size(Q_tvec_centers));​
-trial_idx = nearest_idx3(run_start,​Q_tvec_centers);​ % NOTE: on non-Windows,​ use nearest_idx.m+trial_idx = nearest_idx3(metadata.taskvars.trial_iv.tstart,​Q_tvec_centers);​ % NOTE: on non-Windows,​ use nearest_idx.m
 trial_id(trial_idx) = 1; trial_id(trial_idx) = 1;
 trial_id = cumsum(trial_id);​ trial_id = cumsum(trial_id);​
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 This yields: This yields:
  
-{{ :analysis:cosmo2014:dec_err_1step_250ms.png?600 |}}+{{ :analysis:course-w16:dec_err.png?nolink&600 |}}
  
-Thus, on average our estimate is 1.87 pixels away from the true position. Earlier laps seem to have some more outliers of bins where our estimate is bad (large distance) but there is no obvious trend across laps visible.+(Note, your plot might look a little different.) 
 + 
 +Thus, on average our estimate is 2.14 pixels away from the true position. Earlier laps seem to have some more outliers of bins where our estimate is bad (large distance) but there is no obvious trend across laps visible.
  
 ☛ How does the decoding accuracy depend on the bin size used? Try a range from very small (10ms) to very large (1s) bins, making sure to note the average decoding error for 50ms bins, for comparison with results in the next module. What factors need to be balanced if the goal is maximum accuracy? ☛ How does the decoding accuracy depend on the bin size used? Try a range from very small (10ms) to very large (1s) bins, making sure to note the average decoding error for 50ms bins, for comparison with results in the next module. What factors need to be balanced if the goal is maximum accuracy?
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 This gives: This gives:
  
-{{ :analysis:cosmo2014:2d_decerror_space_250ms.png?600 |}}+{{ :analysis:course-w16:dec_errspace.png?nolink&600 |}}
  
 It looks like the decoding error is on average larger on the central stem, compared to the arms of the maze. It looks like the decoding error is on average larger on the central stem, compared to the arms of the maze.
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 ==== Challenges ==== ==== Challenges ====
  
-Visual inspection of the animation or movie suggests that the decoding does a decent job of tracking the rat's true location. Howeverespecially because of the number of parameters involved in the analysis (bin sizehow firing rates are computedthe Poisson and independence assumptions,​ etc.) it is important to quantify how well we are doing.+★ Implement a decoding ​analysis on your own data. Remember that this does not necessarily requires using spiking data -- anything that you can construct ​tuning curve for would work! In this modulewe had something like 100 simultaneously recorded neuronsbut even if you have only oneyou can still attempt to use it for decodingQuantify decoding performance (errorfor a few relevant parameters. 
 + 
 +★ How does decoding performance scale with the number of cells used? This is an important ​issue if we want to figure out if we should invest resources in attempting to record from more neurons, or if we have all we need in data sets such as this one.
  
-★ Modify the visualization code above to also compute ​a //decoding error// for each frame. This should be the distance between the rat's actual location ​and the location with the highest posterior probability ​(the "​maximum a posteriori"​ or MAP estimate). Plot this error over timeexcluding those bins where no cells were activeHow does this error change over the course ​of the session? How does it change if you reduce the bin size for decoding to 100ms?+★ In famous paper, [[http://science.sciencemag.org/content/318/​5852/​900 | Johnson ​and Redish ​(2007)]] showed that the hippocampus transiently represents possible future trajectories as rats appeared to deliberate between choices (left? right?at a decision pointHoweverthey used a controversial "​two-step"​ decoding algorithm which attracted criticismRefer to the Methods section ​of that paper to figure out how they did the decoding, and modify the code above to implement their version. What differences do you notice?
analysis/course-w16/week10.txt · Last modified: 2018/07/07 10:19 (external edit)