Responses of somatosensory area 2 neurons to actively and passively generated limb movements

London, Brian M.; Miller, Lee E.

doi:10.1152/jn.00372.2012

Cited by 124 publications

(149 citation statements)

References 37 publications

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“…For example, M1 has strong responses to sensory stimuli, especially stretching of the tendons and muscles [56]. In an experiment where responses are driven primarily by externally imposed perturbations of the arm [57,58] it seems likely that M1 would exhibit a neuron-mode structure like that of V1 in the present study. If so, then it would be natural to apply a model in which responses are largely externally driven.…”

Section: Discussionmentioning

confidence: 80%

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

et al. 2016

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Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure—a basic example is the frequency spectrum—and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were ‘simplest’ (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.

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Section: Discussionmentioning

confidence: 80%

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

et al. 2016

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“…A possible explanation could be related to the fact that the descending motor command (or, more specifically, the efference copy), exerts a disruption on the somatosensory cortices (see London & Miller, 2013), thus explaining the observed slowing in RTs during the preparation period. Similarly to the goal-directed reaches investigated in laboratory-like tasks , the movement execution period highlighted significantly faster RTs to tactile stimulation.…”

Section: Discussionmentioning

confidence: 99%

Speed of reaction to sensory stimulation is enhanced during movement

Juravle

Spence

2015

Acta Psychologica

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“…Substantial progress has been made using behavioral and electrophysiological approaches, complemented recently by the use of genetically tractable model systems to access and manipulate discrete motor circuits and examine the behavioral consequences. While we limit our discussion here to cerebellar and spinal circuits, we note that there is substantial evidence implicating multiple regions of the cerebral cortex in the integration of sensory and forward model cues for forelimb movement updating, as discussed elsewhere [31–34]. …”

Section: Neural Substrates For Forward Models and Internal Copiesmentioning

confidence: 99%

Skilled forelimb movements and internal copy motor circuits

Azim

Alstermark

2015

Current Opinion in Neurobiology

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Mammalian skilled forelimb movements are remarkable in their precision, a feature that emerges from the continuous adjustment of motor output. Here we discuss recent progress in bridging the gap between theory and neural implementation in understanding the basis of forelimb motor refinement. One influential theory is that feedback from internal copy motor pathways enables fast prediction, through a forward model of the limb, an idea supported by behavioral studies that have explored how forelimb movements are corrected online and can adapt to changing conditions. In parallel, neural substrates of forelimb internal copy pathways are coming into clearer focus, in part through the use of genetically tractable animal models to isolate spinal and cerebellar circuits and explore their contributions to movement.

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Responses of somatosensory area 2 neurons to actively and passively generated limb movements

Abstract: London BM, Miller LE. Responses of somatosensory area 2 neurons to actively and passively generated limb movements.

Cited by 124 publications

References 37 publications

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

Speed of reaction to sensory stimulation is enhanced during movement

Skilled forelimb movements and internal copy motor circuits

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