Bill's Neuroscience
Bill's Neuroscience
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Principal Component Analysis and High Dimensional Data in R. Part 3
Principal Component Analysis and High Dimensional Data in R. Part 3
Переглядів: 64

Відео

Principal Component Analysis and High Dimensional Data in R. Part 2
Переглядів 514 місяці тому
Principal Component Analysis and High Dimensional Data in R. Part 2
Principal Component Analysis and High Dimensional Data in R. Part 1
Переглядів 884 місяці тому
Principal Component Analysis and High Dimensional Data in R. Part 1
Basics of R. Part 4
Переглядів 174 місяці тому
Basics of R. Part 4
Basics of R. Part 3
Переглядів 114 місяці тому
Basics of R. Part 3
Basics of R. Part 2
Переглядів 134 місяці тому
Basics of R. Part 2
Basics of R. Part 1
Переглядів 274 місяці тому
Basics of R. Part 1
Basics of Clustering in R. Part 4
Переглядів 184 місяці тому
Basics of Clustering in R. Part 4
Basics of Clustering in R. Part 3
Переглядів 94 місяці тому
Basics of Clustering in R. Part 3
Basics of Clustering in R. Part 2
Переглядів 144 місяці тому
Basics of Clustering in R. Part 2
Basics of Clustering in R. Part 1
Переглядів 194 місяці тому
Basics of Clustering in R. Part 1
Presentations I - How to give a scientific poster
Переглядів 556Рік тому
Presentations I - How to give a scientific poster
Presentations II - How to give a scientific talk
Переглядів 126Рік тому
Presentations II - How to give a scientific talk
Biostatistics 11
Переглядів 562 роки тому
We learn about non-parametric models
Biostatistics 10
Переглядів 412 роки тому
We learn about the assumption of independence, why it's so important, and what to do if we violate this assumption.
Biostatistics 9
Переглядів 372 роки тому
Biostatistics 9
Biostatistics 8
Переглядів 352 роки тому
Biostatistics 8
Biostatistics 7
Переглядів 362 роки тому
Biostatistics 7
Biostatistics 6
Переглядів 362 роки тому
Biostatistics 6
Biostatistics 5
Переглядів 412 роки тому
Biostatistics 5
Biostatistics 4
Переглядів 552 роки тому
Biostatistics 4
Biostatistics 3
Переглядів 792 роки тому
Biostatistics 3
Biostatistics 2
Переглядів 1112 роки тому
Biostatistics 2
Biostatistics 1
Переглядів 2282 роки тому
Biostatistics 1
Beginners guide to Machine Learning, Deep Learning and AI
Переглядів 812 роки тому
Beginners guide to Machine Learning, Deep Learning and AI
Epilepsy 7 - Antiepileptic Drugs
Переглядів 2103 роки тому
Epilepsy 7 - Antiepileptic Drugs
Epilepsy 6 - Focus on Absence Epilepsy
Переглядів 1,2 тис.3 роки тому
Epilepsy 6 - Focus on Absence Epilepsy
Epilepsy 5 - Cellular mechanisms of Epilepsy
Переглядів 5413 роки тому
Epilepsy 5 - Cellular mechanisms of Epilepsy
Epilepsy 4 - Causes of Epilepsy
Переглядів 2433 роки тому
Epilepsy 4 - Causes of Epilepsy
Epilepsy 3 - Types of Epilepsy
Переглядів 4793 роки тому
Epilepsy 3 - Types of Epilepsy

КОМЕНТАРІ

  • @danbocker7398
    @danbocker7398 3 місяці тому

    These are really good videos! Ty

  • @ميسالسباك
    @ميسالسباك 4 місяці тому

    Amazing info😊

  • @johnm.ratliff6880
    @johnm.ratliff6880 4 місяці тому

    As an aspiring neuroscientist I really appreciate these videos

  • @johnm.ratliff6880
    @johnm.ratliff6880 5 місяців тому

    love this channel

  • @056vatsalapandey3
    @056vatsalapandey3 6 місяців тому

    hey, great work !

  • @k.butler8740
    @k.butler8740 6 місяців тому

    I wish I saw this content as a senior in high school. Starting with the simple wire characteristics is accessible

  • @vladi1475S
    @vladi1475S 6 місяців тому

    Have you ever try ilastik? If yes how do you think it is compared to others?

    • @bilz0r
      @bilz0r 6 місяців тому

      I've heard of it, but I've never used it, and I don't know anyone who does. But from scanning through their code base, it is being actively maintained, so that is always a good sign. Looks like it would be something useful to be familiar with, because of t's general purpose nature.

  • @emmanuel1227
    @emmanuel1227 7 місяців тому

    Thank you

  • @meenakshivengarai2299
    @meenakshivengarai2299 7 місяців тому

    Loved this so much.. could you please elaborate why the current in voltage clamp is in the opposite direction?

    • @bilz0r
      @bilz0r 7 місяців тому

      Good question. It is fundamentally just a convention. When voltage clamp papers were first being published, some people would show positive current entering the cell via ion channels (and hence being opposed by negative current being applied by the amplifier) as going up the page (i.e. positive). Other people would show it as a negative current (because that is what the amplifier actually did). The convention became to report what the amplifier did. This is because this is actually the truth. As in, if we show the current that the amplifier applied, we are truly, definitely showing what the amplifier applied. We ASSUME (or perhaps hope) that this is equal and opposite to the current flowing through ion channels, but it is never exactly accurate (and in some cases can be very far from accurate). So we stick to what we know happened, and hope it is an accurate representation of what happened at the ion channels.

  • @ishitvvats2044
    @ishitvvats2044 9 місяців тому

    thank you. wonderful video.

  • @macchiato_1881
    @macchiato_1881 9 місяців тому

    Thank you. Very useful for quickstarting reading papers regarding these.

  • @tessacarstairs
    @tessacarstairs 9 місяців тому

    thank god for you good sir 😭

  • @shebasanimations
    @shebasanimations 10 місяців тому

    Thank you for this

  • @arctek1352
    @arctek1352 11 місяців тому

    Agreed with the other commment. Can anyone explain why for every positive charge going into a neuron one must come out? I thought the point is there are imbalances between extra/intraceullar concentrations.

    • @bilz0r
      @bilz0r 11 місяців тому

      I understand the confusion. It is a very common source of difficulty. I think the confusion primarily comes what "current" means when it comes to a capacitor. But the basic answer as to "why" is because "physics". In circuit theory, it's called "Kirchhoff's Current Law", which states that the sum of all currents entering and exiting a node must equal zero, i.e. if a positive charge enters a place, then either a negative charge must enter, or a positive charge must leave. But to try to give you a mental model, imagine a tiny bubble of membrane, with a single sodium channel in the membrane. Inside our bubble there are an equal number of positive and negative charges, and the same applies outside the cell, so the transmembrane voltage is zero. The sodium channel briefly opens, and despite there being no electrochemical gradient, a Na+ ion happens to move from outside the cell, to inside the cell. The instant that happens, the electric field inside the cell changes, with the voltage inside the cell now being positive. This attracts some negative charges outside the cell to the membrane, and repels some positive charges. This net movement of charge IS a current. Indeed, it will (in total) exactly equal and opposite to the current that moved into the cell. This current is usually described as the capacitive current. While the movement of the ion through the ion channel is called the ionic current. So Iion = -Icap. i.e all the movement of charge into a via ion channels cell, ends up charging up the membrane capacitor. This rule only applies in "isopotential spheres" like out membrane bubble. But in large cells, it fundamentally still holds, it's just that there is one more place for current to flow: down dendrites and axons, i.e. axial current. There you can say: Iion = -Icap -Iaxial. I hope that helps.

  • @arctek1352
    @arctek1352 11 місяців тому

    Thank you very very much for these enjoyable videos.

  • @word2826
    @word2826 Рік тому

    Thank uuuuuuuuuuuuuuuuuuuu

  • @ananthakrishnank3208
    @ananthakrishnank3208 Рік тому

    Thanks. This is Interesting! 1:34 I know that brain waves are of four or five types. The highest being Gamma waves (30 to 80 Hz). Now, when I see something like 5000! Hz, I feel I'm missing something.

  • @neurosciencegirl
    @neurosciencegirl Рік тому

    Thanks for doing this video! Great help for my exam :)

  • @rionachmisteilofionnagain9807

    great explanation

  • @rionachmisteilofionnagain9807

    Highly underrated channel. So glad, I came across it.

  • @sceneraycoltd7401
    @sceneraycoltd7401 Рік тому

    good videos, but please take good sleep

  • @sceneraycoltd7401
    @sceneraycoltd7401 Рік тому

    thanks a lot for your sharing

  • @ConnieGuo-m3x
    @ConnieGuo-m3x Рік тому

    Absolutely enjoyed your video series!! Super clear and informative, helped me a lot with understanding terminologies, being able to interpret ephys figures, and more. Keep making videos, please!!!!

  • @zappafile123
    @zappafile123 Рік тому

    Bright enthusiastic student asks: "So is setting up an animal for 2 photon imagining fun?" Jaded neuroscientist says: "Sure its fun, fun in the same way sitting in a bath tub of tabasco sauce and shoving shards of glass up your butt is fun"

  • @ataelbizanti9998
    @ataelbizanti9998 Рік тому

    incredible. just incredible. you have a remarkable gift of making some of the most complex concepts sound so easy and simple. thank you so much for sharing your knowledge online for free.

  • @eduardoabreu78
    @eduardoabreu78 Рік тому

    isn't it True that neuralink killed more primates than most researchers?

  • @Sawmya003
    @Sawmya003 Рік тому

    Very helpful tutorials for beginners!

  • @TheDudeiamthebest
    @TheDudeiamthebest Рік тому

    Great video.

  • @saral.9845
    @saral.9845 Рік тому

    I appreciate your explanations in all of your videos. But the stifled yawning is terrible.

    • @billsneuroscience2598
      @billsneuroscience2598 Рік тому

      Sorry about the yawns. I do feel bad about them. But I had a new born at the time. I'm going to re-record them in the second half of this year hopefully.

  • @Ezekiel647
    @Ezekiel647 Рік тому

    Great 👍

  • @Ion_Photon
    @Ion_Photon Рік тому

    So the 2-photon or multi-photon process doesn't need any real middle energy state (k), as long as they are condensed enough and can add up, the excitation would happen?

  • @sristyhalder6551
    @sristyhalder6551 2 роки тому

    Thank you so much for the detailed explanation..

  • @jehadyasin04
    @jehadyasin04 2 роки тому

    Do you think non-parametric tests with two factors having an effect is a good area of research?

  • @elnazallahverdlo3324
    @elnazallahverdlo3324 2 роки тому

    man I didnt get anything! Im sure there are ways to improve your way of speech!

    • @bilz0r
      @bilz0r 2 роки тому

      I have a New Zealand accent, because I grew up in New Zealand. But if you're having trouble, turn on closed captions.

    • @056vatsalapandey3
      @056vatsalapandey3 6 місяців тому

      what do you mean by 'improve the speech'. the man took out time to explain you. now you want him to 'speak different'?!?!??!!?!??! audacity.

  • @Ingenierie-Projets
    @Ingenierie-Projets 2 роки тому

    Thank you for you efforts

  • @liamhobson2803
    @liamhobson2803 2 роки тому

    This is SUCH a good video. Thank you very much for posting this.

  • @chrisvaccaro229
    @chrisvaccaro229 2 роки тому

    Great video

  • @edoardowijayagrappolini7630
    @edoardowijayagrappolini7630 2 роки тому

    Thanks. The causality discourse you made was very useful for understanding the underlying mechanism!

  • @karolinamazur261
    @karolinamazur261 2 роки тому

    Can whole-cell patch clamp be an in vivo electrophysiology technique?

    • @billsneuroscience2598
      @billsneuroscience2598 2 роки тому

      Can it? Absolutely. It wasn't invented for doing that, but it's pretty common these days.

  • @mohammadrezakhodashenaskha5006
    @mohammadrezakhodashenaskha5006 2 роки тому

    I have just added the equivalent components for the HH model through Linear Circuit. The output of each component is not something like an Action Potential. How can I get that by inserting the same component and values in the Linear Circuit section? Thanks.

  • @mohammadrezakhodashenaskha5006
    @mohammadrezakhodashenaskha5006 2 роки тому

    Thanks for this tutorial. I need to simulate the whole neuron with the electrical circuit. Can I do this by NEURON software itself? If not, which app do you suggest me to use to simulate the neuron with circuits? Thanks

    • @bilz0r
      @bilz0r 2 роки тому

      So, theoretically, you can build up an entire neuron in circuit builder, compartment by compartment. But I would strongly advise against it. Typically, one would build the neuron with Neuron Code (i.e. in a HOC file), then just import the model into circuit builder, which then just appears as a single element in the circuit. Then you can add on whatever circuit you want.

    • @mohammadrezakhodashenaskha5006
      @mohammadrezakhodashenaskha5006 2 роки тому

      @@bilz0r Thanks, I need to do it due to variations of the components. BTW, would you mind if I ask you how I can import the model into Linear Circuit?

    • @billsneuroscience2598
      @billsneuroscience2598 2 роки тому

      @@mohammadrezakhodashenaskha5006 Variation of what components? Like membrane resistance? You don't need the circuit builder for that. You get access to the soma of the cell with the second from bottom component in the Linear Circuit Builder, Arrange menu. Right now I can't remember what you do if you want access to any other component.

    • @mohammadrezakhodashenaskha5006
      @mohammadrezakhodashenaskha5006 2 роки тому

      @@billsneuroscience2598 Thanks but I'm afraid I couldn't understand how I can get access to the cell's soma from the the second from bottom component in the Linear Circuit Builder! You mean the "Keep Connected" toggle?

    • @billsneuroscience2598
      @billsneuroscience2598 2 роки тому

      @@mohammadrezakhodashenaskha5006 No, on the other side of the window. Below the resistor, capacitor... op amp. Second from the bottom.

  • @wiseturtule
    @wiseturtule 3 роки тому

    Thanks for the video!! Quick question: What was the threshold called? Didn´t quite catch that part :)

    • @bilz0r
      @bilz0r 3 роки тому

      Hi Victor, the threshold where the input rate must be above to cause LTP (or be below to cause LTD) is usually called θM or "Theta M"

    • @wiseturtule
      @wiseturtule 3 роки тому

      @@bilz0r Thank you!

    • @wiseturtule
      @wiseturtule 3 роки тому

      @@bilz0r Thank you!