A dot product neuron for hardware implementation of competitive networks

Competitive models based on a simple dot product neuron often deal with normalized vectors, which adds a hard computational cost. Using Euclidean distance nodes without normalization is only a partial solution, because they are less plausible from a biological point of view and the computational cost of the Euclidean distance is greater than that of the dot product. In this work the author proposes a dot product neuron, formally equivalent to a Euclidean neuron, which does not require vector normalization. The only requirement for such a neuron model is subtracting from the dot product an iteratively computed bias. A simple incremental learning rule for this neuron is also introduced. The proposed model is suitable for hardware implementation of competitive networks