Biomedical subjects

Krishna V Shenoy

Publications and source records attributed to Krishna V Shenoy.

10 recordsLinked to original sources

PubMed2006

A central source of movement variability.

Movements are universally, sometimes frustratingly, variable. When such variability causes error, we typically assume that something went wrong during the movement. The same assumption is made by recent and influential models of motor control. These posit that the principal limit on repeatable performance is neuromuscular noise that corrupts movement as it occurs. An alternative hypothesis is that movement variability arises before movements begin, during motor preparation. We examined this possibility directly by recording the preparatory activity of single cortical neurons during a highly practiced reach task. Small variations in preparatory neural activity were predictive of small variations in the upcoming reach. Effect magnitudes were such that at least half of the observed movement variability likely had its source during motor preparation. Thus, even for a highly practiced task, the ability to repeatedly plan the same movement limits our ability to repeatedly execute the same movement.

Animals↗

PubMed2006

Delay of movement caused by disruption of cortical preparatory activity.

We tested the hypothesis that delay-period activity in premotor cortex is essential to movement preparation. During a delayed-reach task, we used subthreshold intracortical microstimulation to disrupt putative "preparatory" activity. Microstimulation led to a highly specific increase in reach reaction time. Effects were largest when activity was disrupted around the time of the go cue. Earlier disruptions, which presumably allowed movement preparation time to recover, had only a weak impact. Furthermore, saccadic reaction time showed little or no increase. Finally, microstimulation of nearby primary motor cortex, even when slightly suprathreshold, had little effect on reach reaction time. These findings provide the first evidence, of a causal and temporally specific nature, that activity in premotor cortex is fundamental to movement preparation. Furthermore, although reaction times were increased, the movements themselves were essentially unperturbed. This supports the suggestion that movement preparation is an active and actively monitored process and that movement can be delayed until inaccuracies are repaired. These results are readily interpreted in the context of the recently developed optimal-subspace hypothesis.

Action Potentials↗

PubMed2006

Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach.

Neurons in premotor and motor cortex show preparatory activity during an instructed-delay task. It has been suggested that such activity primarily reflects visuospatial aspects of the movement, such as target location or reach direction and extent. We asked whether a more dynamic feature, movement speed, is also reflected. Two monkeys were trained to reach at different speeds ("slow" or "fast," peak speed being approximately 50-100% higher for the latter) depending on target color. Targets were presented in seven directions and at two distances. Of 95 neurons with tuned delay-period activity, 95, 78, and 94% showed a significant influence of direction, distance, and instructed speed, respectively. Average peak modulations with respect to direction, distance and speed were 18, 10, and 11 spikes/s. Although robust, modulations of firing rate with target direction were not necessarily invariant: for 45% of neurons, the preferred direction depended significantly on target distance and/or instructed speed. We collected an additional dataset, examining in more detail the effect of target distance (5 distances from 3 to 12 cm in 2 directions). Of 41 neurons with tuned delay-period activity, 85, 83, and 98% showed a significant impact of direction, distance, and instructed speed. Statistical interactions between the effects of distance and instructed speed were common, but it was nevertheless clear that distance "tuning" was not in general a simple consequence of speed tuning. We conclude that delay-period preparatory activity robustly reflects a nonspatial aspect of the upcoming reach. However, it is unclear whether the recorded neural responses conform to any simple reference frame, intrinsic or extrinsic.

Algorithms↗

PubMed2006

A high-performance brain-computer interface.

Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.

Animals↗

PubMed2006

Neural variability in premotor cortex provides a signature of motor preparation.

We present experiments and analyses designed to test the idea that firing rates in premotor cortex become optimized during motor preparation, approaching their ideal values over time. We measured the across-trial variability of neural responses in dorsal premotor cortex of three monkeys performing a delayed-reach task. Such variability was initially high, but declined after target onset, and was maintained at a rough plateau during the delay. An additional decline was observed after the go cue. Between target onset and movement onset, variability declined by an average of 34%. This decline in variability was observed even when mean firing rate changed little. We hypothesize that this effect is related to the progress of motor preparation. In this interpretation, firing rates are initially variable across trials but are brought, over time, to their "appropriate" values, becoming consistent in the process. Consistent with this hypothesis, reaction times were longer if the go cue was presented shortly after target onset, when variability was still high, and were shorter if the go cue was presented well after target onset, when variability had fallen to its plateau. A similar effect was observed for the natural variability in reaction time: longer (shorter) reaction times tended to occur on trials in which firing rates were more (less) variable. These results reveal a remarkable degree of temporal structure in the variability of cortical neurons. The relationship with reaction time argues that the changes in variability approximately track the progress of motor preparation.

Action Potentials↗

PubMed2005

Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems.

A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that state-of-the-art spike sorting algorithms are not only feasible using modern complementary metal oxide semiconductor very large scale integration processes, but may represent the best option for extracting large amounts of data in implantable neural prosthetic interfaces.

Action Potentials↗

PubMed2004

Model-based neural decoding of reaching movements: a maximum likelihood approach.

A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.

Action Potentials↗

PubMed2003

Neural prosthetic control signals from plan activity.

The prospect of assisting disabled patients by translating neural activity from the brain into control signals for prosthetic devices, has flourished in recent years. Current systems rely on neural activity present during natural arm movements. We propose here that neural activity present before or even without natural arm movements can provide an important, and potentially advantageous, source of control signals. To demonstrate how control signals can be derived from such plan activity we performed a computational study with neural activity previously recorded from the posterior parietal cortex of rhesus monkeys planning arm movements. We employed maximum likelihood decoders to estimate movement direction and to drive finite state machines governing when to move. Performance exceeded 90% with as few as 40 neurons.

Action Potentials↗

PubMed2002

Pursuit speed compensation in cortical area MSTd.

When we move forward the visual images on our retinas expand. Humans rely on the focus, or center, of this expansion to estimate their direction of self-motion or heading and, as long as the eyes are still, the retinal focus corresponds to the heading. However, smooth pursuit eye movements add visual motion to the expanding retinal image and displace the focus of expansion. In spite of this, humans accurately judge their heading during pursuit eye movements even though the retinal focus no longer corresponds to the heading. Recent studies in macaque suggest that correction for pursuit may occur in the dorsal aspect of the medial superior temporal area (MSTd); neurons in this area are tuned to the retinal position of the focus and they modify their tuning to partially compensate for the focus shift caused by pursuit. However, the question remains whether these neurons shift focus tuning more at faster pursuit speeds, to compensate for the larger focus shifts created by faster pursuit. To investigate this question, we recorded from 40 MSTd neurons while monkeys made pursuit eye movements at a range of speeds across simulated self- or object motion displays. We found that most MSTd neurons modify their focus tuning more at faster pursuit speeds, consistent with the idea that they encode heading and other motion parameters regardless of pursuit speed. Across the population, the median rate of compensation increase with pursuit speed was 51% as great as required for perfect compensation. We recorded from the same neurons in a simulated pursuit condition, in which gaze was fixed but the entire display counter-rotated to produce the same retinal image as during real pursuit. This condition yielded the result that retinal cues contribute to pursuit compensation; the rate of compensation increase was 30% of that required for accurate encoding of heading. The difference between these two conditions was significant (P < 0.05), indicating that extraretinal cues also contribute significantly. We found a systematic antialignment between preferred pursuit and preferred visual motion directions. Neurons may use this antialignment to combine retinal and extraretinal compensatory cues. These results indicate that many MSTd neurons compensate for pursuit velocity, pursuit direction as previously reported and pursuit speed, and further implicate MSTd as a critical stage in the computation of egomotion.

Algorithms↗

PubMed2002

Response of MSTd neurons to simulated 3D orientation of rotating planes.

We studied whether the dorsal division of the medial superior temporal area (MSTd) in the macaque has activity related to structure-from-motion (SFM) processing. As the simplest form of three-dimensional (3D) structure, we chose a planar stimulus and examined the relation between the neural responses and the simulated 3D orientation of the plane defined by motion cues. We recorded from 114 MSTd neurons while monkeys were performing a visual fixation task. These neurons responded to a basic set of optic flow patterns such as translation, expansion/contraction, and rotation. Responses of these neurons to rotating plane stimuli were examined to see whether the MSTd neurons exhibited selectivity to the tilt and slant that characterize the 3D orientation of the plane. We found that most MSTd neurons tested (97 of 114) responded to the plane stimuli, and many neurons (65 of 97) exhibited selectivity to tilt and/or slant. Of 97 neurons, 18% (17/97) were selective only to tilt, 24% (23/97) only to slant, and 26% (25/97) to both. Control experiments rejected the possibility that the selectivity could be explained solely by the sensitivity to local stimulus components such as local translation, local speed, and local speed gradients. These results suggest that MSTd neurons are sensitive to stimulus features specific to the perceived 3D orientation of the rotating plane stimuli and suggest that area MSTd is involved in SFM processing.

Animals↗