The previously published algorithm18,23 was used to analyse the hierarchical structure of the nervous system networks (Fig. 2). Hierarchical position is shown on the y axis; the adjacency of neurons (roughly, anterior to left, posterior to right) is shown on the x axis. For Fig. 2, some small adjustments to the node positions were made, primarily in the horizontal direction, to clarify the data by removing overlaps. Two adjustments were made in the vertical direction to make neuron positions reflect the preponderance of their output. RIA was moved down to the level of the other neurons that have a majority of output onto motor neurons and muscles. Notably, 88% of RIA chemical output is onto motor neurons and muscles, making it seem that RIA should be considered one of the premotor interneurons. It is probably placed at a higher level of the hierarchy by the algorithm because of its large number of inputs from sensory neurons (10) and layer 3 interneurons (3). It receives input from only a single layer 2 interneuron, RIB (see below). (It has negligible gap junctional connectivity.) In the second case, RIB was moved up, to the next higher layer (interneuron layer 2). Only 10% of RIB chemical output is onto motor neurons and muscles, whereas 43% is onto layer 1 interneurons (including RIA). In addition, 40% of RIB total output is through gap junctions. Of these, 37% of the load is with motor neurons (possibly influencing its placement by the algorithm), 15% with layer 1 interneurons and 47% with layer 2 and above. These were the only two neuron classes that seemed to be placed by the algorithm at a position that did not well represent the preponderance of their output.