Gain Field Encoding of the Kinematics of Both Arms in the Internal Model Enables Flexible Bimanual Action

Yokoi, Atsushi; Hirashima, Masaya; Nozaki, Daichi

doi:10.1523/jneurosci.2982-11.2011

Cited by 60 publications

(86 citation statements)

References 47 publications

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“…To produce different amounts of force for arbitrary combinations of bimanual movements, the motor system, therefore, needs neural circuits that show nonlinear tuning for bimanual actions (Fig. 6 B ; Yokoi et al 2011). One example would be patches of cortex that responded preferentially to a single specific bimanual combination.…”

Section: Resultsmentioning

confidence: 99%

Two Distinct Ipsilateral Cortical Representations for Individuated Finger Movements

2012

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Movements of the upper limb are controlled mostly through the contralateral hemisphere. Although overall activity changes in the ipsilateral motor cortex have been reported, their functional significance remains unclear. Using human functional imaging, we analyzed neural finger representations by studying differences in fine-grained activation patterns for single isometric finger presses. We demonstrate that cortical motor areas encode ipsilateral movements in 2 fundamentally different ways. During unimanual ipsilateral finger presses, primary sensory and motor cortices show, underneath global suppression, finger-specific activity patterns that are nearly identical to those elicited by contralateral mirror-symmetric action. This component vanishes when both motor cortices are functionally engaged during bimanual actions. We suggest that the ipsilateral representation present during unimanual presses arises because otherwise functionally idle circuits are driven by input from the opposite hemisphere. A second type of representation becomes evident in caudal premotor and anterior parietal cortices during bimanual actions. In these regions, ipsilateral actions are represented as nonlinear modulation of activity patterns related to contralateral actions, an encoding scheme that may provide the neural substrate for coordinating bimanual movements. We conclude that ipsilateral cortical representations change their informational content and functional role, depending on the behavioral context.

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Section: Resultsmentioning

confidence: 99%

Two Distinct Ipsilateral Cortical Representations for Individuated Finger Movements

2012

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“…For example, static cues (e.g., color) linked to opposing force fields have very limited ability to reduce interference (Gandolfo et al., 1996, Howard et al., 2013), suggesting that neural activity in relevant motor regions may not be affected by such cues. In contrast other contexts such as different dynamic cues (Cothros et al., 2009, Howard et al., 2012, Howard et al., 2015), concurrent motion of the other arm (Howard et al., 2010, Nozaki et al., 2006, Nozaki and Scott, 2009, Yokoi et al., 2011), lead-ins (Howard et al., 2012, Wainscott et al., 2005), and follow throughs (Howard et al., 2015) often allow substantial learning. We suggest that such situations that act as contexts may simply be ones that lead naturally to different neural states in motor related regions.…”

Section: Discussionmentioning

confidence: 99%

Motor Planning, Not Execution, Separates Motor Memories

Sheahan

Franklin

Wolpert

2016

Neuron

148

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SummaryRecent theories of limb control emphasize motor cortex as a dynamical system, with planning setting the initial neural state, and execution arising from the self-limiting evolution of the intrinsic neural dynamics. Therefore, movements that share an initial trajectory but then diverge might have different neural states during the execution of the identical initial trajectories. We hypothesized that motor adaptation maps neural states to changes in motor command. This predicts that two opposing perturbations, which interfere when experienced over the same movement, could be learned if each is associated with a different plan even if not executed. We show that planning, but not executing, different follow-through movements allow opposing perturbations to be learned simultaneously over the same movement. However, no learning occurs if different follow throughs are executed, but not planned prior to movement initiation. Our results suggest neural, rather than physical states, are the critical factor associated with motor adaptation.

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“…Recently, Yokoi et al (2011) demonstrated a different way in which a multiplicative gain field accounts for internal representations underlying motor memory. They did not dissociate intrinsic and extrinsic reference frames but were able to uncover a different type of gain-field representation.…”

Section: Discussionmentioning

confidence: 99%

“…However, to date, studies of neural encoding in M1 have not investigated a gain-field combination of intrinsic and extrinsic coordinates. A key feature of our current work and the Yokoi et al (2011) study was the dense sampling of the generalization function across combinations of two different features. Intriguingly, several studies of neural activity in M1 that have used analogously dense sampling across combinations of different features appear to provide evidence for gain-field encoding across those features.…”

Section: Discussionmentioning

confidence: 99%

Motor Memory Is Encoded as a Gain-Field Combination of Intrinsic and Extrinsic Action Representations

2012

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Actions can be planned in either an intrinsic (body-based) reference frame or an extrinsic (world-based) frame, and understanding how the internal representations associated with these frames contribute to the learning of motor actions is a key issue in motor control. We studied the internal representation of this learning in human subjects by analyzing generalization patterns across an array of different movement directions and workspaces after training a visuomotor rotation in a single movement direction in one workspace. This provided a dense sampling of the generalization function across intrinsic and extrinsic reference frames, which allowed us to dissociate intrinsic and extrinsic representations and determine the manner in which they contributed to the motor memory for a trained action. A first experiment showed that the generalization pattern reflected a memory that was intermediate between intrinsic and extrinsic representations. A second experiment showed that this intermediate representation could not arise from separate intrinsic and extrinsic learning. Instead, we find that the representation of learning is based on a gain-field combination of local representations in intrinsic and extrinsic coordinates. This gain-field representation generalizes between actions by effectively computing similarity based on the (Mahalanobis) distance across intrinsic and extrinsic coordinates and is in line with neural recordings showing mixed intrinsic-extrinsic representations in motor and parietal cortices.

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Gain Field Encoding of the Kinematics of Both Arms in the Internal Model Enables Flexible Bimanual Action

Cited by 60 publications

References 47 publications

Two Distinct Ipsilateral Cortical Representations for Individuated Finger Movements

Two Distinct Ipsilateral Cortical Representations for Individuated Finger Movements

Motor Planning, Not Execution, Separates Motor Memories

Motor Memory Is Encoded as a Gain-Field Combination of Intrinsic and Extrinsic Action Representations

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