What are these AND gate and OR gate

What are these?

AND gate and OR gate Truth table for an AND gate Truth table for an OR gate A B Q 0 0 0 0 1 1 1 0 0 1 1 1 1

Artificial Neural Networks • Biological Inspiration • Brain vs. Computers • The Perceptron • Multilayer networks • Some Applications 3

Biological inspiration • Animals are able to react adaptively to changes in their external and internal environment, and they use their nervous system to perform these behaviours. • An appropriate model/simulation of the nervous system should be able to produce similar responses and behaviours in artificial systems. • The nervous system is build by relatively simple units, the neurons, so copying their behavior and functionality should be the solution.

Brain and Machine • The Brain – – Pattern Recognition Association Complexity Noise Tolerance • The Machine – Calculation – Precision – Logic

The contrast in architecture • The Von Neumann architecture uses a single processing unit; – Tens of millions of operations per second – Absolute arithmetic precision • The brain uses many slow unreliable processors acting in parallel

The Structure of Neurons • 1011 neurons of at least 20 types. • 1014 synapses • 1 -10 ms cycle time • Signals are noisy “spike trains” of electrical potential • Neurons die off frequently (never replaced) • Compensates for problems by massive parallelism

The Structure of Neurons • A neuron only fires if its input signal exceeds a certain amount (the threshold) in a short time period. • Synapses vary in strength – Good connections allowing a large signal – Slight connections allow only a weak signal. – Synapses can be either excitatory or inhibitory.

The Structure of Neurons • The spikes travelling along the axon of the presynaptic neuron trigger the release of neurotransmitter substances at the synapse. • The neurotransmitters cause excitation or inhibition in the dendrite of the post-synaptic neuron. • The integration of the excitatory and inhibitory signals may produce spikes in the post-synaptic neuron. • The contribution of the signals depends on the strength of the synaptic connection.

Translating this to ANN The Mc. Cullogh-Pitts model: • spikes are interpreted as spike rates; • synaptic strength are translated as synaptic weights; • excitation means positive product between the incoming spike rate and the corresponding synaptic weight; • inhibition means negative product between the incoming spike rate and the corresponding synaptic weight;

The Mc. Culloch-Pitts “Unit” • • Each neuron has a threshold value Each neuron has weighted inputs from other neurons The input signals form a weighted sum If the activation level exceeds the threshold, the neuron “fires”

The Activation Function (a) Is a step function or threshold function (b) Is sigmoid function [ 1/(1+e-x) ] Changing the bias weight Wo, I moves the threshold location.

Any Boolean function can be implemented using a Mc. Culloch and Pitts perceptron -0. 5 -1

What function does perceptron #1 represent? 0. 6 0. 7 0. 8

What function does perceptron #2 represent? 0. 6 - 0. 4 0. 3 0. 8 - 0. 8