Each processing element in an artificial neural net is analogous to a biological neuron

- – An element accepts a certain number of input values (dendrites) and produces a single output value (axon) of either 0 or 1
- – Associated with each input value is a numeric weight (synapse)
- – The effective weight of the element is the sum of the weights multiplied by their respective input values v1 * w1 + v2 * w2 + v3 * w3
- – Each element has a numeric threshold value
- – If the effective weight exceeds the threshold, the unit produces an output value of 1
- – If it does not exceed the threshold, it produces an output value of 0
- – The process of adjusting the weights and threshold values in a neural net is called training
- – A neural net can presumably be trained to produce whatever results are required
- – But think about the complexity of this! Train a computer to recognize any cat in a picture, based on training run with several pictures.