Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment
Figure 4
Example of behavior sequence for up-down behavior.
Proprioception (first row), vision (second row), sparsely encoded RNN activation (third row), fast and slow context activation (forth and fifth row) of teaching signal (left column), mental simulation of trained network (center column) and actual sensory feedback in physical environment (right column) during up-down behavior at position 3 are shown. In proprioception, 4 out of a total of 8 dimensions were plotted (full line: left arm pronation, dashed: left elbow flexion, dot-dash-dot-dash: right shoulder flexion, dotted: right arm pronation). In the case of vision, two lines correspond to the relative position of the object (full line: X-axis, dashed line: Y-axis). Values for proprioception and vision were mapped to the range from 0.0 to 1.0. CTRNN outputs are sparsely encoded. Both in CTRNN outputs and context activation, the y axis of the graph corresponds to each unit from among the output units and context units. A long sideways rectangle thus indicates the activity of a single neuron over many time steps. The first 64 units of output correspond to proprioception and the last 36 units of output correspond to vision. Colors of rectangles indicate activation level, as indicated in the color bar at the lower right. Reach: reach for the object, UD: up-down, Home: return to the home position.