Neural network symmetry supports sensory fusion and motor coordination

The study of binocular control systems has unmasked several advantages linked to the topology of premotor neural networks. Their natural structural symmetry can imbed very complex behaviors in response to sensory patterns such as 1) coordination of vergence and version trajectories in binocular systems, 2) simple fusion of multiple sensory sources with diverse dynamics, 3) automatic selection of appropriate motor strategies and platform coordination, according to bilateral sensory patterns. On-going parameter (circuit) switching can also enable additional modes in each strategy (e.g. saccades vs slow tracking). There are also many levels of symmetry in the circuits controlling limb muscles, for example in the stretch reflex connections of agonist/antagonist muscle groups. The examples here provide evidence that the topological organization of premotor neural networks could play a key role in the process of sensorimotor mapping and in the selection of motor strategies. This is in contrast to the classical approach where desired motor patterns are selected at a cortical level, following several computational stages. These biological aspects can have repercussions in the design of robotic control systems.