Parieto-frontal coding of reaching: an integrated framework

In the last few years, anatomical and physiological studies have provided n ew insights into the organization of the parieto-frontal network underlying visually guided arm-reaching movements in at least three domains. (1) Netw ork architecture. It has been shown that the different classes of neurons e ncoding information relevant to reaching are not confined within individual cortical areas, but are common to different areas, which are generally lin ked by reciprocal association connections. (2) Representation of informatio n. There is evidence suggesting that reach-related populations of neurons d o not encode relevant parameters within pure sensory or motor "reference fr ames", but rather combine them within hybrid dimensions. (3) Visuomotor tra nsformation. It has been proposed that the computation of mo tor commands f or reaching occurs as a simultaneous recruitment of discrete populations of neurons sharing similar properties in different cortical areas, rather tha n as a serial process from vision to movement, engaging different areas at different times. The goal of this paper was to link experimental (neurophys iological and neuroanatomical) and computational aspects within an integrat ed framework to illustrate how different neuronal populations in the pariet o-frontal network operate a collective and distributed computation for reac hing. In this framework, all dynamic (tuning, combinatorial, computational) properties of units are determined by their location relative to three mai n functional axes of the network, the visual-to-somatic, position-direction , and sensory-motor axis. The visual-to-somatic axis is defined by gradient s of activity symmetrical to the central sulcus and distributed over both f rontal and parietal cortices. At least four sets of reach-related signals ( retinal, gaze, arm position/movement direction, muscle output) are represen ted along this axis. This architecture defines informational domains where neurons combine different inputs. The position-direction axis is identified by the regular distribution of information over large populations of neuro ns processing both positional and directional signals (concerning the arm, gaze, visual stimuli, etc.) Therefore, the activity of gaze- and arm-relate d neurons can represent virtual three-dimensional (3D) pathways for gaze sh ifts or hand movement. Virtual 3D pathways are thus defined by a combinatio n of directional and positional information. The sensory-motor axis is defi ned by neurons displaying different temporal relationships with the differe nt reach-related signals, such as target presentation, preparation for inte nded arm movement, onset of movements, etc. These properties reflect the co mputation performed by local networks, which are formed by two types of pro cessing units: matching and condition units. Matching units relate differen t neural representations of virtual 3D pathways for gaze or hand, and can p redict motor commands and their sensory consequences. Depending on the unit s involved, different matching operations can be learned in the network, re sulting in the acquisition of different visuo-motor transformations, such a s those underlying reaching to foveated targets, reaching to extrafoveal ta rgets, and visual tracking of hand movement trajectory. Condition units lin k these matching operations to reinforcement contingencies and therefore ca n shape the collective neural recruitment along the three axes of the netwo rk. This will result in a progressive match of retinal, gaze, arm, and musc le signals suitable for moving the hand toward the target.