NEURAL NETWORKS CHAPTER 20,SECTION 5 Chapter 20.Section 5 1
Neural networks Chapter 20, Section 5 Chapter 20, Section 5 1
Outline ◇Brains ◇ Neural networks ◇Perceptrons Multilayer perceptrons Applications of neural networks Chapter 20,Section 5 2
Outline ♦ Brains ♦ Neural networks ♦ Perceptrons ♦ Multilayer perceptrons ♦ Applications of neural networks Chapter 20, Section 5 2
Brains 1011 neurons of >20 types,1014 synapses,1ms-10ms cycle time Signals are noisy "spike trains"of electrical potential Axonal arborization Q Axon from another cell Synapse Dendrite Axon Nucleus Synapses Cell body or Soma Chapter 20.Section 5 3
Brains 1011 neurons of > 20 types, 1014 synapses, 1ms–10ms cycle time Signals are noisy “spike trains” of electrical potential Axon Cell body or Soma Nucleus Dendrite Synapses Axonal arborization Axon from another cell Synapse Chapter 20, Section 5 3
McCulloch-Pitts "unit" Output is a "squashed"linear function of the inputs: ai-g(in)=g(②jW.a) Bias Weight a0=-1 Wo.i ai=g(inj) 形a In, a Input Input Output Links Activation Output Function Function Links A gross oversimplification of real neurons,but its purpose is to develop understanding of what networks of simple units can do Chapter 20,Section 5 4
McCulloch–Pitts “unit” Output is a “squashed” linear function of the inputs: ai ← g(ini) = g ΣjWj,iaj Output Σ Input Links Activation Function Input Function Output Links a0 = −1 ai = g(ini) ai g W ini j,i W0,i Bias Weight aj A gross oversimplification of real neurons, but its purpose is to develop understanding of what networks of simple units can do Chapter 20, Section 5 4
Activation functions g(in) g(ini) +1 ini (a) (b) (a)is a step function or threshold function (b)is a sigmoid function 1/(1+e-*) Changing the bias weight Wo.:moves the threshold location Chapter 20.Section 55
Activation functions (a) (b) +1 +1 ini ini g(ini g(in ) i) (a) is a step function or threshold function (b) is a sigmoid function 1/(1 + e−x) Changing the bias weight W0,i moves the threshold location Chapter 20, Section 5 5