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Matlab Tutorial: Spike data visualisation - Plot Raster | PSTH | SDF

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  • Опубликовано: 28 фев 2020
  • This video explains how to visualise spiking data in an efficient way. I cover raster plots to demonstrate raw, trial-wise data but also show how to visualise the average spiking activity in a peristimulus time histogram (PSTH) or with a spike density function (SDF).
    Credit belongs to Alwin Gieselmann, PhD (Newcastle University). This video is based on his functions.
    Script and example data can be downloaded here: drive.google.c...

Комментарии • 17

  • @mrblackmamba117
    @mrblackmamba117 2 года назад

    Underrated video. Thank you. Please upload more

  • @mjollnirboy
    @mjollnirboy 21 день назад

    Thanks a lot for this video. Two Questions:
    #1. Can I analyse Spiking Data in Pickle Files using MATLAB?
    #2. Can MATLAB handle data derived from DVS cameras?

  • @aaaaa-de1dh
    @aaaaa-de1dh 2 года назад +1

    GREAT!Thank you very much!

  • @user-se5kp6lu5d
    @user-se5kp6lu5d Год назад

    Hello, Thanks for your great video. To use the codes written for the brain signal (EEG) or physiological signals, such as GSR in DEAP dataset what actions or changes in the codes should be applied.

  • @la9188
    @la9188 3 года назад +3

    Would it be possible to create more similar videos to learn how to analyse spike data?

    • @la9188
      @la9188 3 года назад

      If I want to transform the timestamps into z-score, how can I do that? Do I need the process the data in a particular way? And how do I run an anova with timestamps?

  • @cesarvb1452
    @cesarvb1452 Год назад

    Hello, awesome video it helped me a lot. Although I have a question: to convert the graph of x = #bin, y = #(spikes in that bin) to the usual PSTH where you need frquency, i.e: a graph where x = #bin and y = #frequency, you use the formula (#spikes in a fixed bin /#trails)(#bins that fit in a second), I'm losing my mind here trying to understand why you used that formula any hint???

    • @cesarvb1452
      @cesarvb1452 Год назад +1

      Forget it I just realized, if you do (# spikes in a fixed bin / # trails) you have the average spikes per bin ie: (spikes / bin), but if you multiply that by the number of bins that fit in a second you get the average spikes per second (spikes / s) = Hz. Man it was easy I feel dumb I've been here for like two hours trying to understand this

  • @ronyut1984
    @ronyut1984 3 года назад +2

    The background music is really annoying, anyway thanks for the great tutorial.

    • @felixschneider5040
      @felixschneider5040  Год назад

      Coming back to this video after some time, I totally agree! What have I done 😅

  • @alisiddique5262
    @alisiddique5262 4 года назад

    GREAT

  • @niharikadighe312
    @niharikadighe312 Год назад

    How did you create the sptimes cell array?

    • @felixschneider5040
      @felixschneider5040  Год назад

      I've extracted the spike times from the raw data file. How to do that depends on the recording system you're working with and the data processing during recording (e.g. filtering, thresholding etc).

  • @elinakc2163
    @elinakc2163 3 года назад

    hi..did you import data from neuroexplorer? Please more detailed videos..Thank you.

    • @felixschneider5040
      @felixschneider5040  3 года назад

      Hi Elina, I have imported the data from the Neuralynx file format using functions that are freely available on their homepage. Let me know if you need more information.

    • @elinakc2163
      @elinakc2163 3 года назад

      @@felixschneider5040 I followed your tutorial to visualize my data but i got lost at 1:47 . Btw, how many minutes data should we take to plot PSTH/raster?
      Thanks :)

    • @felixschneider5040
      @felixschneider5040  3 года назад

      Hi Elina, script and example data are freely available. Check out the link in the description.
      Regarding your question: there is no simple answer, I‘m afraid. It all depends on your experimental paradigm. Personally, I would recommend to get as many repetitions of the same stimulus condition as you possibly can.