Comparing a brain-inspired robot action selection mechanism with ‘winner-takes-all’

We present a new robotic implementation of a brain-inspired model of action selection described by Gurney et al. (Gurney et al., 2001a, Gurney et al., 2001b) based on neural circuits located in the basal ganglia and thalamus of the vertebrate brain. Compared to an earlier robot implementation (Montes-Gonzalez et al., 2000), the new model demonstrates the capacity of the selection system to produce efficient ‘energy’ consumption/conversion in a ‘feeding/resting’ task whilst maintaining essential state variables within a ‘zone of viability’. Generating appropriate action selection in this new setting entailed using biologically plausible Sigma-Pi units that can exploit correlated and anti-correlated dependencies between input signals when computing the ‘salience’ (urgency) of competing actions. A comparison between this brain-inspired selection mechanism and classical ‘winner-takes-all’ showed that the former can provide better behavioral persistence leading to more efficient energy intake