Differential Representation of Perception and Action in the Frontal Cortex

Schwartz, Andrew B.; Moran, Daniel W.; Reina, Giulio

doi:10.1126/science.1087788

Cited by 114 publications

(73 citation statements)

References 20 publications

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“…The PVA has enabled investigation of cortical control of arm movement (e.g., Moran and Schwartz 1999) including phenomena such as illusion perception (Schwartz et al 2004) and has also been used in quite different contexts, such as the representation of moving tactile stimuli in sensory cortex (Ruiz et al 1995). The method originated from the observation that motor cortex neurons are directionally tuned, with broad tuning curves that may be characterized reasonably well by 2 parameters, average firing rate and preferred direction.…”

Section: The Population Vector Algorithm Is Simple and Often Effectivementioning

confidence: 99%

“…Examples include position representation by ensembles of rat hippocampal neurons (Brown et al 1998;Zhang et al 1998), velocity encoding by fly H1 neurons (Bialek et al 1991), velocity and position encoding by M1 neurons (Georgopoulos et al 1986;Moran and Schwartz 1999;Serruya et al 2002), and natural scene representations by cat lateral geniculate nucleus neurons (Stanley et al 1999). Aside from their use to study how populations of neurons represent information, decoding algorithms are being studied for their use in the brain-controlled neural prosthetic devices (Schwartz et al 2004;Serruya et al 2002, Wessberg et al 2000.…”

Section: P O P U L a T I O N C O D I N G A N D D E C O D I N Gmentioning

confidence: 99%

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Statistical Issues in the Analysis of Neuronal Data

Kass¹,

Ventura²,

Brown³

2005

Journal of Neurophysiology

250

239

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Kass, Robert E., Valérie Ventura, and Emery N. Brown. Statistical issues in the analysis of neuronal data. J Neurophysiol 94: 8 -25, 2005; doi:10.1152/jn.00648.2004. Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews wellestablished statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data. I N T R O D U C T I O NTechnical advances have made available new methods for collecting, storing, and manipulating electrophysiological data. Investigations may now not only characterize neuronal activity in anatomically well defined regions, but they can also examine dynamics of neuronal response and their relationship to behavior. Although elementary methods of data analysis [such as t-tests or visual examination of the peristimulus time histogram (PSTH)] remain useful for many purposes, the growing complexity of neuroscientific experiments, often examining subtle changes on a comparatively fine timescale, requires careful attention to statistical methods for data analysis. In this overview we discuss some of the fundamental data analytical issues that face researchers in neurophysiology, illustrating the general points with the problems of describing the evolution of a neuron's firing rate across time, finding accurate population codes, and assessing time-varying correlation between 2 neurons. In each case recent work has provided a statistical technique that outperforms previous methodology, boosting the scientific information as effectively as if the number of experimental trials, or the number of neurons, had been increased by a substantial factor. We also indicate some of the ways modern statistical procedures can accommodate important complexities, such as dynamic changes in temporal and spatial aspects of hippocampal place cell firing and trial-to-trial variability in cortical neurons recorded from behaving animals. Our review supplements the brief and general guidance offered by Curran-Everett and Benos (2004), and may be regarded as an update to the early work of Perkel et al. (1967a,b).The new field of computational neuroscience uses detailed biophysical models and artificial neural networks to study emergent behavior of neural systems and the way neural systems represent and transmit information (e.g...

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Section: The Population Vector Algorithm Is Simple and Often Effectivementioning

confidence: 99%

Section: P O P U L a T I O N C O D I N G A N D D E C O D I N Gmentioning

confidence: 99%

Statistical Issues in the Analysis of Neuronal Data

Kass¹,

Ventura²,

Brown³

2005

Journal of Neurophysiology

250

239

View full text Add to dashboard Cite

show abstract

“…The design of teleoperation systems for 0893 such applications is required to carefully consider the integration of different sensory modalities, which motivates studies of human perception-action models, as outlined in Section 3. It is well known that humans are visually dominated (Schwartz, Moran, & Reina, 2004) and it is thus of interest to understand how this influences integration of haptics and graphics. At the same time there are clear models that suggest that much of human actions are feedforward driven rather than feedback driven (Berthoz, 2000).…”

Section: Introductionmentioning

confidence: 99%

Teleoperation for a ball-catching task with significant dynamics

2008

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a b s t r a c tIn this paper we present ongoing work on how to incorporate human motion models into the design of a high performance teleoperation platform. A short description of human motion models used for ball-catching is followed by a more detailed study of a teleoperation platform on which to conduct experiments. Also, a pilot study using minimum jerk theory to explain user input behavior in teleoperated catching is presented.

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“…The advantage of this setup is that placing the monitor above the head allows unencumbered arm and hand movements in front of the subject while still limiting what the subject sees to only those virtual images that the experimenter wants the subject to see. This type of system has been used for both BMI studies where the virtual limb image is controlled directly by the decoded brains signals (Taylor, et al, 2002;Schwartz et al, 2006) and for basic motor control studies where the virtual limb image is moved based on actual limb movements captured in real time via position sensors (Moran & Schwartz, 1999;Schwartz, et al, 2004). One disadvantage of this configuration is that the monitor is placed close to the top of the head where it can contaminate the neural recordings with electromagnetic (EM) noise.…”

Section: Pros and Cons Of Different Stereo Viewing Systemsmentioning

confidence: 99%

“…Here again, the convenience of instantly being able to place targets of any size or shape at any location throughout the workspace makes conducting experiments in a virtual environment much more efficient than trying to accomplish the same tests by setting up physical targets. In addition, the virtual world enables the experimenter to skew or perturb the visual feedback of the movement to test different hypotheses about how movements are controlled (Schwartz, et al, 2004).…”

Section: Introduction Virtual Reality In Brain-machine Interfacing Anmentioning

confidence: 99%

Virtual reality hardware and graphic display options for brain–machine interfaces

Marathe

Carey

Taylor

2008

Journal of Neuroscience Methods

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Virtual reality hardware and graphic displays are reviewed here as a development environment for brain-machine interfaces (BMIs). Two desktop stereoscopic monitors and one 2D monitor were compared in a visual depth discrimination task and in a 3D target-matching task where ablebodied individuals used actual hand movements to match a virtual hand to different target hands. Three graphic representations of the hand were compared: a plain sphere, a sphere attached to the fingertip of a realistic hand and arm, and a stylized pacman-like hand. Several subjects had great difficulty using either stereo monitor for depth perception when perspective size cues were removed. A mismatch in stereo and size cues generated inappropriate depth illusions. This phenomenon has implications for choosing target and virtual hand sizes in BMI experiments. Target matching accuracy was about as good with the 2D monitor as with either 3D monitor. However, users achieved this accuracy by exploring the boundaries of the hand in the target with carefully controlled movements. This method of determining relative depth may not be possible in BMI experiments if movement control is more limited. Intuitive depth cues, such as including a virtual arm, can significantly improve depth perception accuracy with or without stereo viewing.

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Differential Representation of Perception and Action in the Frontal Cortex

Cited by 114 publications

References 20 publications

Statistical Issues in the Analysis of Neuronal Data

Statistical Issues in the Analysis of Neuronal Data

Teleoperation for a ball-catching task with significant dynamics

Virtual reality hardware and graphic display options for brain–machine interfaces

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