Neural Networks and the Computational Brain

Embodiment: Why simulation is all we need
(a) The network exhibits low

“Behavior control experiments require an agent with a body!”

This claim – that embodiment is essential to understanding neuro-control, behavior generation and cognition on artificial agents – is usually not a matter of debate any more. There is great consensus that the body significantly influences the types of (possible) behaviors and the way they are generated. This seems so self-evident today that one might wonder why there ever was a discussion.

Questions to answer about neuro-control therefore do not only focus on the (neuro-)control aspects of behavior, but also on the role of the body. An example would be the investigation of how much of the ‘cognition’ or control of a behavior is actually realized in the neurodynamics of the controller, and how much just is an effect of the body. A good example are simple bang-bang reflex-loop controllers, where the motor neurons of a joint are periodically ‘switched’ between their maximal and minimal firing rate as a reaction to the angle sensors of the joints. The output of a motor neuron is hereby interpreted as the desired joint angle. Although the network only produces two (alternating) motor outputs, the joint is moved back and forth in a more or less smooth motion trajectory. That trajectory is not generated by a sophisticated neural network, but instead originates from the constraints of the body. The motors simply have a maximal velocity and need some time to approach the desired angular position. Without a body, such a network would not produce anything useful, because its entire dynamics heavily depends on the characteristics of the motors and the sensors, that – through the body and the environment – close the control loop.

Source: CyberCodez: Coding with Brains

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