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Artificial Intelligence Methods Neural Networks Lecture 1 Rakesh K. Bissoondeeal ([email protected]) Biological Neural Networks Biological Neuron Synapses - Gap between adjacent neurons across which chemicals are transmitted: input Dendrites - Receive synaptic contacts from other neurons Cell body/Soma - Metabolic centre of the neuron: processing Axon - produces the output Artificial Neuron Artificial neurons are the building blocks of Artificial Neural Networks Artificial Neurons Artificial neurons simulate the four basic functions of natural neurons - Signals are passed between neurons over connection links - Each connection link has an associated weight which multiplies the signal transmitted - Each neuron applies an activation function to is net input (sum of weighted input signals) to produce an output signal Why study Artificial Neural Networks Desire to understand the brain and to imitate some of its strength Traditional computers implement a sequence of logical and arithmetic operations but don’t have the ability to adapt their structure or learn Learn from examples, Generalisation Used to solved task where it is beneficial to use a machine but impossible to program all possible outcomes Applications List of applications mentioned in the literature Aerospace -high performance aircraft autopilot Banking –check and other document reading Defence – weapon steering Financial –financial analysis Speech – speech recognition Brief History of ANNs 1943 W.S. McCulloch and W. Pitts - Original idea published 1949 D. Hebb - Publishes ideas on learning in biological neurons 1958 F. Rosenblatt - First practical working networks called perceptrons Brief History of ANNs 1969 . M Minsky and S. Papert - Rubbish ANNs - Most research on ANNs stop 1970s Widrow, Parker and others - Low level of activity - Backpropagation invented 1980s Rumelhart and others - Rediscovery of Backpropagation - Revival of interest in ANNs McCulloch-Pitts Neuron First mathematical model of the biological neuron - Mc Culloch and Pitts (1943) Most models used today are descended from McCulloch and Pitts neuron McCulloch-Pitts Neuron The output of a neuron is binary. That is, the neuron either fires (output of one) or does not fire (output of zero). X1 2 X2 2 Y -1 X3 McCulloch-Pitts Neuron Neurons in a McCulloch-Pitts network are connected by directed, weighted paths A connection path is excitatory if the weight on the path is positive; otherwise it is inhibitory X1 2 X2 2 Y -1 X3 McCulloch-Pitts Neuron Each neuron has a fixed threshold (θ). If the net input to the neuron is greater than the threshold, the neuron fires X1 2 If net input >= θ, output=1 X2 If net input < θ, output = 0 2 Y -1 X3 Example 1 Logic Functions: AND x1 x2 AND True=1, False=0 If both inputs true, output true Else, output false Threshold(Y)=2 0 0 0 1 0 0 1 1 0 1 0 1 X1 1 Y X2 1 AND Function Example 2 Logic Functions: OR x1 x2 OR True=1, False=0 If either of inputs true, output true Else, output false Threshold(Y)=2 0 0 0 1 0 1 1 1 0 1 1 1 X1 2 Y X2 2 OR Function McCulloch-Pitts Neuron Structure does not change - Fixed system that takes inputs to produce output Has no concept of learning However, McCulloch-Pitts Neuron forms the foundation of modern ANNs - Changes made to allow learning Recommended Reading Fundamentals of neural networks; Architectures, Algorithms and Applications, L. Fausett, 1994. Artificial Intelligence: A Modern Approach, S. Russel and P. Norvig, 1995. An Introduction to Neural Networks. 2nd Edition, Morton, IM.