Sensory population decoding for visually-guided movements

Autor: Kris S. Chaisanguanthum, Stephen G. Lisberger, Sonja S. Hohl
Jazyk: angličtina
Rok vydání: 2013
Předmět:
Male
genetic structures
Eye Movements
Motion Perception
Action Potentials
0302 clinical medicine
Models
Psychology
Visual Cortex
Neurons
0303 health sciences
education.field_of_study
General Neuroscience
medicine.anatomical_structure
Neurological
Cognitive Sciences
Smooth
Decoding methods
Neural decoding
Neuroscience(all)
Clinical Trials and Supportive Activities
Population
Models
Neurological

Sensory system
Smooth pursuit
Article
03 medical and health sciences
Clinical Research
medicine
Reaction Time
Animals
Motion perception
education
Eye Disease and Disorders of Vision
030304 developmental biology
Neurology & Neurosurgery
business.industry
Neurosciences
Eye movement
Pattern recognition
Macaca mulatta
Pursuit
Smooth

Visual cortex
Pursuit
Artificial intelligence
business
Neuroscience
030217 neurology & neurosurgery
Photic Stimulation
Zdroj: Neuron, vol 79, iss 1
Popis: SummaryWe have used a new approach to study the neural decoding function that converts the population response in extrastriate area MT into estimates of target motion to drive smooth pursuit eye movement. Experiments reveal significant trial-by-trial correlations between the responses of MT neurons and the initiation of pursuit. The preponderance of significant correlations and the relatively low reduction in noise between MT and the behavioral output support the hypothesis of a sensory origin for at least some of the trial-by-trial variation in pursuit initiation. The finding of mainly positive MT-pursuit correlations, whether the target speed is faster or slower than the neuron’s preferred speed, places strong constraints on the neural decoding computation. We propose that decoding is based on normalizing a weighted population vector of opponent motion responses; normalization comes from neurons uncorrelated with those used to compute the weighted population vector.
Databáze: OpenAIRE