  The neural networks of the brain are capable of learning statistical input
regularities on the basis of synaptic learning, functional integration into
increasingly larger, interconnected neural assemblies, and self organization.
This self organizing ability has implications for biologically inspired control
structures in robotics. On the basis of signal input from vision, sound, smell,
touch and proprioception, multisensory representations for action are generated
on the basis of physically specified input from the environment. The
somatosensory cortex is a brain hub that delivers a choice example of
integration for multifunctional representation and control. All sensory
information is in a first instance topologically represented in the biological
brain, and thereafter integrated in somatosensory neural networks for
multimodal and multifunctional control of complex behaviors. Multisignal input
triggers interactions between visual, auditory, tactile, olfactive, and
proprioceptive mechanisms, which cooperate or compete during learning, and
contribute to the formation of integrated representations for action,
reflection, and communication between the agent and the outside world.
Interaction fuels complex behavioral strategy deployment and further learning
for increasingly coherent representation of intrinsically ambiguous physical
environments.
