State of the art: A central problem in motor research is to understand how sensory signals, and in particular visual inputs, are used to plan and execute goal-directed movements. It is classically assumed that this process involves a set of coordinate transformation starting in early visual areas and ending in motor areas. However, it is becoming increasingly clear that this hierarchical vision of visuo-motor behvior is incomplete and motor control critically builds on the dynamic processing of both visual information in motor cortical areas (Riehle 2005; Confais et al. 2012) and motor information in visual areas (Mirabella et al. 2007).
Objectives and methods: To address the issue of visuomotor coupling during complex motor tasks, we are performing massively parallel recordings of neuronal activity simultaneously from visual, parietal and motor areas, obtained in macaque monkeys trained in continuous movements towards a sequence of visual targets. For this purpose, we are using multiple micro-electrode (“Utah”) arrays simultaneously implanted in several cortical areas (Riehle, Brochier). Our analysis rely on two types of neuronal signals: (i) LFPs, low frequency continuous signals, mostly representing the local average of synaptic (input) activities, and (ii) spiking activities of individual neurons, a discrete 0-1 signal, showing the spiking (output) activity. Both signals are recorded simultaneously from each electrode in multiple cortical areas. This project is at the core of an international collaboration with the INM6, Forschungszentrum Jülich (Germany).
Expected results: We first expect to provide evidence of the reciprocal influence of motor related activity in visual areas and of visual processing in motor areas. We will then characterize the complex dynamic of this coupling at different stages of movement planning en execution.
Expected candidate profile: The ideal candidate should be motivated to use electrophysiological recording techniques in awake macaque monkeys. Basic experience with programming is required for data analysis and control of the experimental setup (Matlab, Python).