The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the "latent dynamics". These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.

Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

words)Translational studies on motor control and neurological disorders require detailed monitoring of sensorimotor components of natural limb movements in relevant animal models. However, available experimental tools do not provide a sufficiently rich repertoire of behavioral signals. Here, we developed a robotic platform that enables the monitoring of kinematics, interaction forces, and neurophysiological signals during user-definable upper limb tasks for monkeys. We configured the platform to position instrumented objects in a three-dimensional workspace and provide an interactive dynamic force-field. We show the relevance of our platform for fundamental and translational studies with three example applications. First, we study the kinematics of natural grasp in response to variable interaction forces. We then show simultaneous and independent encoding of kinematic and forces in single unit intra-cortical recordings from sensorimotor cortical areas. Lastly, we demonstrate the relevance of our platform to develop clinically relevant brain computer interfaces in a kinematically unconstrained motor task control of the robot arm, 3) a synchronized interaction force recording system, 4) a strain-gauge grip pressure sensor, 5) an infrared video tracking system to measure three-dimensional joint kinematics (Vicon, Oxford, UK) and an 6) electrophysiology system (Blackrock Microsystems, Salt Lake City, USA). We assessed the versatility and efficacy of our framework by programming a robotic task for monkeys. We configured the robot to position objects in a three-dimensional workspace and trained monkeys to reach and pull on the objects while kinetic, kinematic and neural signals were simultaneously recorded. Closed-Loop control infrastructureThe IIWA robotic arm features a large workspace (Figure S1) allowing ranges of motion that are compatible with both human and monkey reaches. Additionally, the robotic arm is able to actively lift up to 7Kg of weight, which makes it robust to manipulation by monkeys.We developed a software package that implements a real-time closed-loop control (Figure 2A, 10.5281/zenodo.3234138) configured as a finite state machine. This allows fast configuration of tasks that proceed through several phases, where each phase requires a different behaviour of the robot.In our specific example application, at the beginning of the trial, the robot moves the end effector to a predetermined position in space using impedance joint control. Upon reaching position, the state machine switches to mass-spring damper behaviour (Figure 2A). Stiffness and damping parameters are definable by the user. Variations from this behaviour can be easily configured using our software.