 At one level of analysis, the basic computational element of a neural network, biological or artificial, is the nerve cell, or neuron. Mathematically, neurons can be represented as simple computational devices, transforming inputs into output. Both inputs and outputs reflect biological variables. For our discussion, we assume that neuronal inputs are frequencies of action potentials (neuronal “spikes”) in the axons whose terminal branches synapse onto the neuron in question, while neuronal output is the frequency of action potentials generated in its axon after processing the inputs. A neuron thereby computes its total input, usually treated mathematically as the sum of the products of the signal strength along each input line times the synaptic weight on that line. It then computes a new activation state based on its total input and current activation state, and a new output state based on its new activation value. The neuron’s output state is transmitted as a signal strength to whatever neurons its axon synapses on. The output state reflects systematically the neuron’s new activation  state.[3]