V. 21:4 (74-79): Neural Networks by Wolf von Rönik

Neural Networks by Wolf von Rönik

Think neural networks are too difficult a concept to learn? They’re not. Take a look.

In a broad sense, neural nets are computer models that attempt to mimic the “wetware” or actual physiological structure and functioning of the brain (which is outlined in the sidebar, “Neurons”)....In the simple model displayed in Figure 1, I1, I2, and I3 represent inputs. Moving down, each input reaches a weight (synapse) labeled W1, W2, and W3 where input and corresponding weight are multiplied. This new information is conducted (dendrite) as input to the node (soma). Inside the node, the input-weight products are summed. This sum is then sent as output along the single output (axon) arrow on the right to be compared with a desired historical response. If the output and the historical response agree (that is, a threshold is reached), you follow the lower arrow and the model receives new input. If they don’t agree, you return to the weights via the top arrow, revalue the weights, and repeat the process until the output and desired historical response agree.