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Lecture 3: Learning and Memory. Prof.dr. Jaap Murre University of Maastricht University of Amsterdam jaap@murre.com http://neuromod.uva.nl. Overview. We will study Hebbian learning and the formation of categories We will do some basic memory experiments Examine various forms of memory
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Lecture 3:Learning and Memory Prof.dr. Jaap Murre University of Maastricht University of Amsterdam jaap@murre.com http://neuromod.uva.nl
Overview • We will study Hebbian learning and the formation of categories • We will do some basic memory experiments • Examine various forms of memory • We will try to locate memory in the brain and relate brain lesions to amnesia • We will also briefly explore executive functions in the frontal lobes • We will look at memory improvement
With Hebbian learning, two learning methods are possible • With unsupervised learning there is no teacher: the network tries to discern regularities in the input patterns • With supervised learning an input is associated with an output • If the input and output are the same, we speak of auto-associative learning • If they are different it is called hetero-associative learning
Supervised learning with Hopfield (1982) network • Bipolar activations • -1 or 1 • Symmetric weights (no self weights) • wij= wji • Asynchronous update rule • Select one neuron randomly and update it • Simple threshold rule for updating
Energy of a Hopfield network Energy E = - ½i,jwjiaiaj E = - ½i(wjiai+ wijai)aj = - iwjiai aj Net input to node j is iwjiai = netj Thus, we can write E = - netj aj
The energy minimization question can also be turned around • Given ai and aj, how should we set the weight wji = wji so that the energy is minimized? • E = - ½ wjiaiaj, so that • when aiaj = 1, wji must be positive • when aiaj = -1, wji must be negative • For example, wji= aiaj, where is a learning constant
Hebb and Hopfield • When used with Hopfield type activation rules, the Hebb learning rule places patterns at attractors • If a network has n nodes, 0.15n random patterns can be reliably stored by such a system • For complete retrieval it is typically necessary to present the network with over 90% of the original pattern
We will look at an example of competitive learning • Competitive learning is a form of unsupervised learning
Example of competitive learning:Stimulus ‘at’ is presented 1 2 a t o
Example of competitive learning:Competition starts at category level 1 2 a t o
Example of competitive learning:Competition resolves 1 2 a t o
Example of competitive learning:Hebbian learning takes place 1 2 a t o Category node 2 now represents ‘at’
Presenting ‘to’ leads to activation of category node 1 1 2 a t o
Presenting ‘to’ leads to activation of category node 1 1 2 a t o
Presenting ‘to’ leads to activation of category node 1 1 2 a t o
Presenting ‘to’ leads to activation of category node 1 1 2 a t o
Category 1 is established through Hebbian learning as well 1 2 a t o Category node 1 now represents ‘to’
Before we continue... • Everybody on the right of the classroom, please, close their eyes until the following words have been presented • The others, pay attention to the following 10 words. You will be asked to remember them later • Don’t write them down!
Now for the other half... • Everybody on the left of the classroom, please, close their eyes until the following words have been presented • The others, pay attention to the following 10 words. You will be asked to remember them later • Don’t write them down!
Memory and attention are strongly intertwined • Paying attention can be seen as holding in memory • Attention is required for rehearsal • The longer an item is attended (held in memory), the higher the chance it will be remembered later
Desimone’s study of V4* neurons * V4 is visual cortex before inferotemporal cortex (IT)
Brown-Peterson task • Try to remember three letters, e.g., XJC • When given a number (e.g., 307), start counting backward in threes (307, 304, 301, 298, …) • When the Write! text appears, write down the letters you remember • This has to be done at least several times to obtain the effect