title: Fast remapping of sensory stimuli onto motor actions on the basis of contextual modulation.
abstract: Higher organisms can establish complex associations between sensory events and motor responses. More remarkable than their complexity, however, is that the resulting sensory-motor maps can be selectively interchanged. For example, a person who speaks English and Spanish can read aloud "con once, sin once," going effortlessly from one language to the other. What is the neural basis of this capacity? Here, a network model is presented in which multiple maps between sensory stimuli and motor actions are possible, but only one of them, depending on behavioral context, is implemented at any given time. The key is a nonlinear representation in which the gain of sensory responses is regulated by context information. Neuronal responses can indeed show variations in gain, as has been documented in the case of proprioceptive signals such as eye and head position, which can modulate visually triggered activity. However, in contrast to these, the contextual cues used here need not bear any relationship to the physical attributes of the stimuli; in particular, spatial location is irrelevant. The model thus postulates the existence of sensory neurons that are nonlinearly modulated by arbitrary context signals, a plausible and testable prediction. The proposed mechanism allows a network of neurons to effectively change the functional connectivity between its inputs and outputs and may partially explain how animals can quickly adapt their behavior to varying environmental conditions.
authors.affiliation: Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157-1010, USA. esalinas@wfubmc.edu
authors.forename: Emilio
authors.initials: E
authors.lastname: Salinas
mesh_terms: D003198:Computer Simulation; D003463:Cues; D005133:Eye Movements / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009475:Neurons, Afferent / Q000502:physiology; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance / Q000502:physiology*