analysis:nsb2016:week10
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analysis:nsb2016:week10 [2016/07/04 18:42] mvdm created |
analysis:nsb2016:week10 [2018/07/07 10:19] (current) |
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| LoadMetadata; | LoadMetadata; | ||
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| + | % ENCoding variable names, for data used to estimate tuning curves, which capture | ||
| + | % the relationship between some variable and firing rate (here: "How is position | ||
| + | % (en)coded by the neurons we are recording?"), as opposed to DECoding variable names, | ||
| + | % which are used for data we want to decode (e.g. replay). | ||
| ENC_S = restrict(S,metadata.taskvars.trial_iv); | ENC_S = restrict(S,metadata.taskvars.trial_iv); | ||
| ENC_pos = restrict(pos,metadata.taskvars.trial_iv); | ENC_pos = restrict(pos,metadata.taskvars.trial_iv); | ||
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| The data in this module computes tuning curves for location, but the idea is of course more general. For continuous variables in particular, it is a natural and powerful way to describe the relationship between two quantities -- spikes and location in this case, but there is no reason why you couldn't do something like pupil diameter as a function of arm reaching direction, for instance! | The data in this module computes tuning curves for location, but the idea is of course more general. For continuous variables in particular, it is a natural and powerful way to describe the relationship between two quantities -- spikes and location in this case, but there is no reason why you couldn't do something like pupil diameter as a function of arm reaching direction, for instance! | ||
| + | |||
| + | ☛ Try plotting some place fields for your own data. You might want to restrict the data to those times when the rat is moving around; see [[http://ctnsrv.uwaterloo.ca/vandermeerlab/doku.php?id=analysis:nsb2016:week16&s[]=getlinspd#step_2estimating_place_fields | here]] for an example of how to do this. | ||
| ==== Bayesian decoding ==== | ==== Bayesian decoding ==== | ||
analysis/nsb2016/week10.1467672142.txt.gz · Last modified: 2018/07/07 10:19 (external edit)
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