We recorded the activity of single neurons in the posterior parietal cortex (area LIP) of two rhesus monkeys while they discriminated the direction of motion in random-dot visual stimuli. The visual task was similar to a motion discrimination task that has been used in previous investigations of motion-sensitive regions of the extrastriate cortex. The monkeys were trained to decide whether the direction of motion was toward one of two choice targets that appeared on either side of the random-dot stimulus. At the end of the trial, the monkeys reported their direction judgment by making an eye movement to the appropriate target. We studied neurons in LIP that exhibited spatially selective persistent activity during delayed saccadic eye movement tasks. These neurons are thought to carry high-level signals appropriate for identifying salient visual targets and for guiding saccadic eye movements. We arranged the motion discrimination task so that one of the choice targets was in the LIP neuron's response field (RF) while the other target was positioned well away from the RF. During motion viewing, neurons in LIP altered their firing rate in a manner that predicted the saccadic eye movement that the monkey would make at the end of the trial. The activity thus predicted the monkey's judgment of motion direction. This predictive activity began early in the motion-viewing period and became increasingly reliable as the monkey viewed the random-dot motion. The neural activity predicted the monkey's direction judgment on both easy and difficult trials (strong and weak motion), whether or not the judgment was correct. In addition, the timing and magnitude of the response was affected by the strength of the motion signal in the stimulus. When the direction of motion was toward the RF, stronger motion led to larger neural responses earlier in the motion-viewing period. When motion was away from the RF, stronger motion led to greater suppression of ongoing activity. Thus the activity of single neurons in area LIP reflects both the direction of an impending gaze shift and the quality of the sensory information that instructs such a response. The time course of the neural response suggests that LIP accumulates sensory signals relevant to the selection of a target for an eye movement.

/pdf/neural-basis-of-a-perceptual-decision-in-the-parietal-cortex-bkka3jq1y1.pdf

Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

https://www.ai.rug.nl/~lambert/projects/BCI/literature/serious/invasive/neural-implants-plbi-01-02-carmena.pdf

Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

The neural bases of behavior are often discussed in terms of perceptual, cognitive, and motor stages, defined within an information processing framework that was originally inspired by models of human abstract problem solving. Here, we review a growing body of neurophysiological data that is difficult to reconcile with this influential theoretical perspective. As an alternative foundation for interpreting neural data, we consider frameworks borrowed from ethology, which emphasize the kinds of real-time interactive behaviors that animals have engaged in for millions of years. In particular, we discuss an ethologically-inspired view of interactive behavior as simultaneous processes that specify potential motor actions and select between them. We review how recent neurophysiological data from diverse cortical and subcortical regions appear more compatible with this parallel view than with the classical view of serial information processing stages.

https://www.cisek.org/pavel/Pubs/CisekKalaska2010.pdf

Neural Mechanisms for Interacting with a World Full of Action Choices

Skilled motor behaviour, from the graceful leap of a ballerina to a precise pitch by a baseball player, appears effortless but reflects an intimate interaction between the complex mechanical properties of the body and control by a highly distributed circuit in the CNS An important challenge for understanding motor function is to connect these three levels of the motor system — motor behaviour, limb mechanics and neural control Optimal feedback control theory might provide the important link across these levels of the motor system and help to unravel how the primary motor cortex and other regions of the brain plan and control movement

/pdf/optimal-feedback-control-and-the-neural-basis-of-volitional-2ypl1d3rcs.pdf

Optimal feedback control and the neural basis of volitional motor control

The functions of a cortical area are determined by its extrinsic connections and intrinsic properties. Using the database CoCoMac, we show that each cortical area has a unique pattern of corticocortical connections — a ‘connectional fingerprint’. We present examples of such fingerprints and use statistical analysis to show that no two areas share identical patterns. We suggest that the connectional fingerprint underlies the observed cell-firing differences between areas during different tasks. We refer to this pattern as a ‘functional fingerprint’ and present examples of such fingerprints. In addition to electrophysiological analysis, functional fingerprints can be determined by functional brain imaging. We argue that imaging provides a useful way to define such fingerprints because it is possible to compare activations across many cortical areas and across a wide range of tasks.

/pdf/the-anatomical-basis-of-functional-localization-in-the-2wuzlrzxt1.pdf

The anatomical basis of functional localization in the cortex

Shen, Liming and Garrett E. Alexander. Neural correlates of a spatial sensory-to-motor transformation in primary motor cortex. J. Neurophysiol. 77: 1171–1194, 1997. Primary motor cortex (MC) has be...

Neural Correlates of a Spatial Sensory-To-Motor Transformation in Primary Motor Cortex

Shen, Liming and Garrett E. Alexander. Preferential representation of instructed target location versus limb trajectory in dorsal premotor area. J. Neurophysiol. 77: 1195–1212, 1997. The dorsal pre...

Preferential representation of instructed target location versus limb trajectory in dorsal premotor area

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Neural Correlates of a Spatial Sensory-To-Motor Transformation in Primary Motor Cortex

Preferential representation of instructed target location versus limb trajectory in dorsal premotor area