I describe a silicon network consisting of a group of excitatory neu(cid:173) rons and a global inhibitory neuron. The output of the inhibitory neuron is normalized with respect to the input strengths. This out(cid:173) put models the normalization property of the wide-field direction(cid:173) selective cells in the fly visual system. This normalizing property is also useful in any system where we wish the output signal to code only the strength of the inputs, and not be dependent on the num(cid:173) ber of inputs. The circuitry in each neuron is equivalent to that in Lazzaro's winner-take-all (WTA) circuit with one additional tran(cid:173) sistor and a voltage reference. Just as in Lazzaro's circuit, the outputs of the excitatory neurons code the neuron with the largest input. The difference here is that multiple winners can be chosen. By varying the voltage reference of the neuron, the network can transition between a soft-max behavior and a hard WTA behav(cid:173) ior. I show results from a fabricated chip of 20 neurons in a 1.2J.Lm CMOS technology.