From sensory space to motor commands: lessons from saccades

Our distributed model of the saccadic system faithfully reproduces saccadic waveforms and the patterns of neuronal activity observed in several brain areas. However, our model is not based on principles found in classical theories of motor control. In this paper we attempt to extract from our model some general principles about neural motor controllers. We conclude that intrinsic brain signals might represent non-physical signals, such as desired sensory states, approximate motor drives, and distributed motor commands, rather than physical signals (e.g., desired displacement or motor error). Furthermore, our model demonstrates that the critical transformation from maps of sensory space to temporal motor commands is not necessarily carried out explicitly. Instead, the transformation can be implicit, emerging from network connections within a feedback loop.