Abstract: A method is implemented in hardware or software type neural network, the neural network is constructed of neurons or neuron circuits each having only one significant processing element in the form of a multiplier. Each neuron applying a gating function to each of neural network inputs. The neural network utilizes a training algorithm which does not require repetitive training and which yields a global minimum to each given set of input vectors.

Abstract: A circuit and method for computing an exponential signal x.sup.g is provided. The circuit includes a logarithm converter which converts an input signal to binary word that represents the logarithm of an input signal x. A first shift register shifts the binary word in a bit-wise fashion to produce a first intermediate value; while a second shift register shifts the binary word in a bit-wise fashion to produce a second intermediate value. The shift registers may be implemented using multiplexers. The shifting operations are equivalent to multiplying the intermediate values by a factor which is a power of two. The first intermediate value is either added to or subtracted from the second intermediate value to produce a combined value. An inverse-logarithm converter converts the combined value to the exponential signal.

Abstract: A method for storing data in a memory device is provided. The method stores blocks of data elements in a two-dimensional address space such that the blocks overlap in the address space. The data elements may be digital words which represent exponents of a polynomial expansion.

Abstract: An artificial neuron, which may be implemented on an integrated circuit, has only one significant processing element in the form of an adder. Inputs are first fed through gating functions to produce gated inputs. These gated inputs are then added together to produce a sum which is added to a weight to produce the neuron output.