## Artificial Neural Network Structures

### BibTeX

@MISC{Has_artificialneural,

author = {This Chapter Has},

title = {Artificial Neural Network Structures},

year = {}

}

### OpenURL

### Abstract

adjustments of the strengths of the synaptic inputs, which led to the incorporation of adjustable synaptic weights on the input lines to excite or inhibit incoming signals. Figure 3.2 - A Neuron with Hebbian Learning Ability Figure 3.2 incorporates adjustable synaptic weights (knobs) on the input lines. An input vector x = (x 1 ,...,x N ), considered to be a columnmatrix vector, is linearly combined with the weight vector w=(w 1 ,...,w N ) via the inner (dot) product to form the sum s= (n=1,N) w n x n = w ox (3-1) If the sum s is greater than the given threshold , then the output y is 1, else it is 0. This threshold function is unipolar in that it puts out the nonnegative values of 0 or 1 (or 0 or V for some voltage V) and complies with the formerly presumed two-valued all-or-nothing principle of biological neurons. Neurons that use the bipolar threshold functions with output values of-1 or 1 (or-V or V for some voltage V) are nowadays called McCulloch-Pitts neurons. For furthe