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COMP305
Biology-Inspired Algorithms
          Ross McFarlane




                           Tutorial 1
Admin
               Monday, 1400–1500
              Ashton Lecture Theatre
                  Friday, 1300–1400
                   Rendall Building

•   Attendance is optional

•   Lecture notes: www.csc.liv.ac.uk/~ivb

•   Tutorial news: www.csc.liv.ac.uk/~rossmcf/teaching
Ion Channel
                              !quot;#         !quot;#

                      !quot;#
                                    !quot;#     !quot;#
              !quot;#           !quot;#

                       !quot;#
                                  !quot;#     !quot;#                 Outside
              !quot;#                           !quot;#
                              !quot;#


                              !quot;#


Membrane                      !quot;#



        !quot;#
                                                  !quot;#
                              !quot;#
                !quot;#
  !quot;#                                     !quot;#
                                                        !quot;#
                                                              Inside
Biology
•Natural         Biology-Inspired
•Experimental      Algorithms
•Computational

                 •Artificial Neural Networks
                 •Evolutionary Algorithms
                 •...
Bioinformatics
McCulloch-Pitts Neuron
a0
a1          q    X
...
an
McCulloch-Pitts Neuron
t    Time step


θ    Threshold

ai   Input from presynaptic neuron i

Wi   Weight of input from presynaptic neuron i


X    Output
McCulloch-Pitts Neuron
                                     n
X   t+1
          = 1 ⇐⇒ s =         t                t
                                          Wi ai       >θ
                                     i=0
    Output is 1 if and only if weighted sum of inputs is
               greater than the threshold.


           while Wi > 0 ∀ai > 0
        So long as no inhibitory inputs are active.
McCulloch-Pitts Neuron
Check inputs from ALL inhibitory
         connections

                 Yes

                                   No
                                                  =0
    All inputs from inhibitory              t+1
    connections equal to 0?             X

                 Yes
                                                  (continued)
continued

Calculate the instant state of the neuron
                   n
           st =         Wi at
                            i
                  i=0

                        Yes


             t
           s ≥θ               ?             No
                                                 X   t+1
                                                           =0

                        Yes


          X      t+1
                        =1

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Comp305 Tutorial 1

  • 1. COMP305 Biology-Inspired Algorithms Ross McFarlane Tutorial 1
  • 2. Admin Monday, 1400–1500 Ashton Lecture Theatre Friday, 1300–1400 Rendall Building • Attendance is optional • Lecture notes: www.csc.liv.ac.uk/~ivb • Tutorial news: www.csc.liv.ac.uk/~rossmcf/teaching
  • 3. Ion Channel !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# Outside !quot;# !quot;# !quot;# !quot;# Membrane !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# !quot;# Inside
  • 4. Biology •Natural Biology-Inspired •Experimental Algorithms •Computational •Artificial Neural Networks •Evolutionary Algorithms •... Bioinformatics
  • 6. McCulloch-Pitts Neuron t Time step θ Threshold ai Input from presynaptic neuron i Wi Weight of input from presynaptic neuron i X Output
  • 7. McCulloch-Pitts Neuron n X t+1 = 1 ⇐⇒ s = t t Wi ai >θ i=0 Output is 1 if and only if weighted sum of inputs is greater than the threshold. while Wi > 0 ∀ai > 0 So long as no inhibitory inputs are active.
  • 8. McCulloch-Pitts Neuron Check inputs from ALL inhibitory connections Yes No =0 All inputs from inhibitory t+1 connections equal to 0? X Yes (continued)
  • 9. continued Calculate the instant state of the neuron n st = Wi at i i=0 Yes t s ≥θ ? No X t+1 =0 Yes X t+1 =1