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one of the learned stimuli

Activity of a single neuron in the cortex. one of the learned stimuli. new stimulus. Hebbian plasticity.

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one of the learned stimuli

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  1. Activity of a single neuron in the cortex one of the learned stimuli new stimulus

  2. Hebbian plasticity “When an axon of cell A is near enough to excite cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased” Donald Hebb, 1949 “Neurons that fire together wire together”

  3. A cortical network

  4. A cortical network

  5. A cortical network

  6. A cortical network

  7. A cortical network

  8. A cortical network

  9. Hebbian plasticity

  10. A cortical network Network can sustain activity even in the absence of input

  11. Specificity of sustained activity

  12. Specificity of sustained activity

  13. Specificity of sustained activity

  14. Specificity of sustained activity

  15. Specificity of sustained activity

  16. A model of associative memory

  17. A model of associative memory

  18. A model of associative memory

  19. A model of associative memory

  20. A model of associative memory

  21. A model of associative memory

  22. A model of associative memory

  23. A model of associative memory

  24. A model of associative memory

  25. A model of associative memory

  26. A model of associative memory

  27. A model of associative memory

  28. A model of associative memory

  29. A model of associative memory

  30. A model of associative memory

  31. A model of associative memory

  32. A model of associative memory

  33. ‘Biological’ memories • Associative: recall is based on content rather than on the address • A transient cue induces a sustained recall • Robust to minor failures of the hardware • Distributed

  34. The mathematical model I will use a slightly different model than the one presented in the last 10 minutes of Wednesday’s class

  35. The mathematical model Neurons are binary: The activity of neuron i, Si= 0,1 at time t+1 input to neuron i at time t

  36. The mathematical model 5 J51 4 1 J21 2 3 J32

  37. The mathematical model A memory pattern is a vector of desired neural activities 5 For example: 4 1 2 3

  38. The Hopfield model trialn +1

  39. The Hopfield model

  40. The Hopfield model 5 J51 4 1 • local learning rule • incremental, on-line J21 2 3 J32 “Neurons that fire together wire together”

  41. The Hopfield model Network connections are symmetrical. It can be shown that with asynchronous updating, the dynamics necessarily converge to a fixed point. • Questions: • What are the fixed points of the dynamics? • What is their relation with the memory pattern?

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