alexa Selectivity for grip type and action goal in macaque inferior parietal and ventral premotor grasping neurons.
Engineering

Engineering

International Journal of Swarm Intelligence and Evolutionary Computation

Author(s): Bonini L, Ugolotti Serventi F, Bruni S, Maranesi M, Bimbi M,

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Abstract Grasping objects requires the selection of specific grip postures in relation to the objects' physical properties. Furthermore, grasping acts can be embedded in actions aimed at different goals, depending on the context in which the action is performed. Here we assessed whether information on grip and action type integrate at the single-neuron level within the parieto-frontal motor system. For this purpose, we trained three monkeys to perform simple grasp-to-eat and grasp-to-place actions, depending on contextual cues, in which different grip types were required in relation to target features. We recorded 173 grasping neurons: 86 from the inferior parietal area PFG and 87 from the ventral premotor area F5. Results showed that most neurons in both areas are selective for grip type, but the discharge of many of them, particularly in PFG, appears to differ in relation to action context. Kinematics data and control experiments indicated that neuronal selectivity appears more likely to depend on the action goal triggered by the context than on specific contextual elements. The temporal dynamics of grip and goal selectivity showed that grasping neurons reflect first "how" the object has to be grasped (grip), to guide and monitor the hand shaping phase, and then "why" the action is performed (goal), very likely to facilitate subsequent motor acts following grasping. These findings suggest that, in the parieto-frontal system, grip types and action goals are processed by both parallel and converging pathways, and area PFG appears to be particularly relevant for integrating this information for action organization. This article was published in J Neurophysiol and referenced in International Journal of Swarm Intelligence and Evolutionary Computation

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