Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Zhuang, Katie Z.; Lebedev, Mikhail А.; Nicolelis, Miguel A. L.

doi:10.1152/jn.00031.2013

Cited by 10 publications

(7 citation statements)

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“…While the findings of Churchland and his colleagues can be viewed as an extension of this approach and a method to produce a visually appealing phase plot, we doubt that they have discovered any new neurophysiological mechanism, as claimed in their article. Indeed, the mere fact that neurons in a population respond at different times with respect to a go-cue tells very little about the function of these responses and does not discard any of the previously proposed representational interpretations, such as the suggestion that neuronal populations perform a sensorimotor transformation (Georgopoulos, Lurito et al 1989, Kalaska 1991, Kakei, Hoffman et al 2003 or handle specific motor parameters (Georgopoulos, Kalaska et al 1982, Georgopoulos, Ashe et al 1992, Kakei, Hoffman et al 1999, Zhuang, Lebedev et al 2014). Cortical activity is certainly dynamical in the sense that neuronal rates change in time, but understanding this dynamic requires a much more thorough analysis than the method proposed by Churchland et al…”

Section: Discussionmentioning

confidence: 99%

“…It is well known that individual neurons in cortical motor areas transiently modulate their firing rates following a stimulus that triggers the production of a voluntary movement (Evarts 1972). These neuronal modulations have been shown to represent various motor parameters, for example movement direction (Georgopoulos, Kalaska et al 1982), although the specifics of these representations are still a matter of debate (Georgopoulos, Ashe et al 1992, Kakei, Hoffman et al 1999, Zhuang, Lebedev et al 2014. With the development of multichannel recordings (Nicolelis, Dimitrov et al 2003, Schwarz, Lebedev et al 2014, it has become possible to study modulations recorded in multiple cortical neurons simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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What, if anything, is the true neurophysiological significance of “rotational dynamics”?

Lebedev

Ossadtchi

et al. 2019

Preprint

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Back in 2012, Churchland and his colleagues proposed that "rotational dynamics", uncovered through linear transformations of multidimensional neuronal data, represents a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland's own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal shift in peak firing rates exhibited by individual neurons, and (2) the temporal sequence of peak rates remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; no special provisions were needed for modeling motor cortical responses. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland's data with multiple linear regression. While these observations suggest that "rotational dynamics" depict an orderly activation of different neurons in a population, we express doubt about Churchland et al.'s exaggerated assessment that this activity pattern is related to "an unexpected yet surprisingly simple structure in the population response" which "explains many of the confusing features of individual neural responses." Instead, we argue that their rotational dynamics approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What, if anything, is the true neurophysiological significance of “rotational dynamics”?

Lebedev

Ossadtchi

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is well known that individual neurons in cortical motor areas transiently modulate their firing rates following a stimulus that triggers the production of a voluntary movement 1 . These neuronal modulations have been shown to represent various motor parameters, for example movement direction 2 , although the specifics of these representations are still a matter of debate 3–5 . With the development of multichannel recordings 6,7 , it has become possible to study modulations recorded in multiple cortical neurons simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics

Lebedev

Ossadtchi

Mill

et al. 2019

Sci Rep

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Back in 2012, Churchland and his colleagues proposed that “rotational dynamics”, uncovered through linear transformations of multidimensional neuronal data, represent a fundamental type of neuronal population processing in a variety of organisms, from the isolated leech central nervous system to the primate motor cortex. Here, we evaluated this claim using Churchland’s own data and simple simulations of neuronal responses. We observed that rotational patterns occurred in neuronal populations when (1) there was a temporal sequence in peak firing rates exhibited by individual neurons, and (2) this sequence remained consistent across different experimental conditions. Provided that such a temporal order of peak firing rates existed, rotational patterns could be easily obtained using a rather arbitrary computer simulation of neural activity; modeling of any realistic properties of motor cortical responses was not needed. Additionally, arbitrary traces, such as Lissajous curves, could be easily obtained from Churchland’s data with multiple linear regression. While these observations suggest that temporal sequences of neuronal responses could be visualized as rotations with various methods, we express doubt about Churchland et al.’s bold assessment that such rotations are related to “an unexpected yet surprisingly simple structure in the population response”, which “explains many of the confusing features of individual neural responses”. Instead, we argue that their approach provides little, if any, insight on the underlying neuronal mechanisms employed by neuronal ensembles to encode motor behaviors in any species.

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“…Additionally, it is possible to predict myoelectric (EMG) signals in the arm using the activity of certain M1 units (Pohlmeyer et al, 2007 ; Ethier et al, 2012 ; Oby et al, 2013 ). Zhuang et al computed joint cross-correlations between neurons and surface EMG of arm muscles in monkeys performing center-out reaching or touchpad pressing (Zhuang et al, 2014 ). They showed unit-EMG cross-correlations were time-varying, involved multiple significant muscle interactions per unit, and did not always have opposite signs for antagonistic muscles, further indicating the correspondence between M1 neurons and muscles is distributed and dynamic.…”

Section: Scientific Contributionsmentioning

confidence: 99%

Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience

Schroeder

Chestek

2016

Front. Neurosci.

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Brain-machine interfaces (BMIs) decode brain activity to control external devices. Over the past two decades, the BMI community has grown tremendously and reached some impressive milestones, including the first human clinical trials using chronically implanted intracortical electrodes. It has also contributed experimental paradigms and important findings to basic neuroscience. In this review, we discuss neuroscience achievements stemming from BMI research, specifically that based upon upper limb prosthetic control with intracortical microelectrodes. We will focus on three main areas: first, we discuss progress in neural coding of reaches in motor cortex, describing recent results linking high dimensional representations of cortical activity to muscle activation. Next, we describe recent findings on learning and plasticity in motor cortex on various time scales. Finally, we discuss how bidirectional BMIs have led to better understanding of somatosensation in and related to motor cortex.

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Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Cited by 10 publications

References 91 publications

What, if anything, is the true neurophysiological significance of “rotational dynamics”?

What, if anything, is the true neurophysiological significance of “rotational dynamics”?

Analysis of neuronal ensemble activity reveals the pitfalls and shortcomings of rotation dynamics

Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience

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