title: Brain control and information transfer.
abstract: In this review, we examine the importance of having a body as essential for the brain to transfer information about the outside world to generate appropriate motor responses. We discuss the context-dependent conditioning of the motor control neural circuits and its dependence on the completion of feedback loops, which is in close agreement with the insights of Hebb and colleagues, who have stressed that for learning to occur the body must be intact and able to interact with the outside world. Finally, we apply information theory to data from published studies to evaluate the robustness of the neuronal signals obtained by bypassing the body (as used for brain-machine interfaces) versus via the body to move in the world. We show that recording from a group of neurons that bypasses the body exhibits a vastly degraded level of transfer of information as compared to that of an entire brain using the body to engage in the normal execution of behaviour. We conclude that body sensations provide more than just feedback for movements; they sustain the necessary transfer of information as animals explore their environment, thereby creating associations through learning. This work has implications for the development of brain-machine interfaces used to move external devices.
authors: Brain Institute, UFRN, Av. Nascimento de Castro 2155, Natal, 59056-450, Brazil. tehovnikej@gmail.com.
authors: Edward J
authors: EJ
authors: Tehovnik
authors: Otolaryngology and Communicative Sciences, Ophthalmology and Neurobiology and Anatomical Sciences, University of Mississippi Medical Center, Jackson, MS, 39216, USA.
authors: Lewis L
authors: LL
authors: Chen
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D056228:Feedback, Sensory / Q000502:physiology*; D006801:Humans; D007257:Information Theory; D009043:Motor Activity / Q000502:physiology*; D014836:Volition / Q000502:physiology*
keywords: Brain–machine interfaces; Feedback; Information theory; Learning; Sensorimotor behaviour; Systems neuroscience