Year: 2015
Type: other
Abstract: Understanding the effects of various sources of noise and ambiguities can change one's views on the two faces of the sensorimotor transformation. This chapter explains how visual motion information is encoded in neural populations and the relationships between these population activities and behaviors. Second, it summarizes how nonsensory, predictive signals can help maintain a good performance when sensory evidences become highly unreliable and, on the contrary, when the future sensory inputs become highly predictable. The chapter illustrates these aspects with behavioral results gathered in both human and nonhuman primates. It shows that these different results on visually guided and predictive smooth pursuit dynamics can be reconciled within a single Bayesian framework. The chapter finally proposes a biologically plausible architecture implementing a hierarchical inference network for a closed-loop, visuomotor control of tracking eye movements.
Language: English
Topic: Motor Control and Adaptation
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W1796654261