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Adams et al. argue that, since the brain is fast and requires little energy, researching biomimetic solutions can help solve the problems that robots have limited energy resources and computing power.

Biomimetic approaches have been ascribed various benefits.

Although Adams et al. argue that biomimetic approaches to robotics promise less energy consumption and processing requirements, they implicitly acknowledge that using spiking neural networks will increase these requirements and is only feasible, if at all, because of recent developments in software and hardware.

Braitenberg postulates the "the law of uphill analysis and downhill invention", which states that it is easier to build something and see what it does (what it can do) than to analyse something just from the observable output.

Neurorobotics is an activity which creates embodied cognitive agents.

Schenck summarizes three neurorobotic studies in which he evaluates visual prediction, and, more specifically, predictive remapping. He argues that his experiments support a claim in psychology saying that pre-saccadic activation of neurons whose receptive fields will contain the location of a salient stimulus after the saccade is not just pre-activation but actually a prediction of what the visual field will be like after the saccade.

Biomimetic (neural) robotics can provide feedback to neuroscience.

Neurorobotics is a sub-field of biorobotics where it is concerned with testing biological hypotheses.

Through simulations of neurons (and neuron ensembles), numbers of neurons can be monitored over time scales which both are not possible in vivo.

This is mainly an argument in favor of computational neuroscience. It is not so valid for ANN in classical AI where neuronal models are quite detached from biological neurons.

According to Rucci et al., neuroscientists can use robots to quantitatively test and analyze their theories.

The degree to which neuroscientists can draw conclusions from computational models depends on biological accuracy.