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week 3

Q: What happens when a neurotransmitter falls in love with a receptor? A: You get a binding relationship. Q: What did the Hollywood film director say after he ...

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week 3

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    Slide 1:CS 182 Sections 101 - 102

    Klinton Bicknell (kbicknell {at} berkeley.edu) Feb 2, 2005 Q: What did the hippocampus say during its retirement speech? A: “Thanks for the memories” Q: What happens when a neurotransmitter falls in love with a receptor? A: You get a binding relationship. Q: What did the Hollywood film director say after he finished making a movie about myelin? A: “That’s a wrap!” http://faculty.washington.edu/chudler/jokes.html

    Slide 2:Announcements

    a2 is out very shortly, due next Tuesday 11:59pm play with tlearn you can either run it on inst machines or download it and run on your pc get started early! new website: www.icsi.berkeley.edu/~klinton/class office hours: 3-4pm Wed. 1308 Dwinelle (in Ling Dept.) Quiz on Thursday

    Slide 3:Where we stand

    Last Week Neural development Basic idea of learning, Hebbs’ rule Connectionist representations This Week Psycholinguistics experiments Spreading Activation, triangle nodes Coming up Backprop (review your Calculus!)

    Slide 4:Quiz!

    What are the different types of memory? Why is Hebb’s rule not the complete story for the learning that goes on in the brain? What’s a McCullough-Pitts neuron? How does it work? What does the “They all rose” experiment show? How can you explain the results computationally?

    Slide 5:Two ways of looking at memory:

    Slide 6:Two ways of looking at memory:

    LTP (hours to days)

    Slide 7:Hebb’s Rule: neurons that fire together wire together Long Term Potentiation (LTP) is the biological basis of Hebb’s Rule Calcium channels is the key mechanism

    LTP and Hebb’s Rule

    Slide 8:Why is Hebb’s rule incomplete?

    here’s a contrived example: should you “punish” all the connections?

    Slide 9:The McCullough-Pitts Neuron

    yj: output from unit j Wij: weight on connection from j to i xi: weighted sum of input to unit i

    Slide 10:Let’s try an example: the OR function

    Assume you have a threshold function centered at the origin What should you set w01, w02 and w0b to be so that you can get the right answers for y0?

    Slide 11:Many answers would work

    y = f (w01i1 + w02i2 + w0bb) recall the threshold function the seperation happens when w01i1 + w02i2 + w0bb = 0 move things around and you get i2 = - (w01/w02)i1 - (w0bb/w02)

    Slide 12:for more information

    check the wikipedia: www.wikipedia.org

    Slide 13:“They all rose”

    triangle nodes: when two of the neurons fire, the third also fires model of spreading activation

    Slide 14:How we can model the triangle node with McCullough-Pitts Neurons?

    A B C

    Slide 15:Anonymous Feedback: Lectures

    feel free to comment on each instructor seperately How is the pace? Do you find the material interesting? too dense? too slow? What will be most helpful to you in getting the most out of lectures? Any particularly confusing topic?

    Slide 16:Anonymous Feedback: Sections

    Have sections been useful? Any feedback on our styles of presentation? How will sections be most helpful to you? Any other comments?

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