Literature DB >> 31924610

Divisively Normalized Integration of Multisensory Error Information Develops Motor Memories Specific to Vision and Proprioception.

Takuji Hayashi1,2,3, Yutaro Kato4, Daichi Nozaki5.   

Abstract

Both visual and proprioceptive information contribute to the accuracy of limb movement, but the mechanism of integration of these different modality signals for movement control and learning remains controversial. We aimed to elucidate the mechanism of multisensory integration for motor adaptation by evaluating single-trial adaptation (i.e., aftereffect) induced by visual and proprioceptive perturbations while male and female human participants performed reaching movements. The force-channel method was used to precisely impose several combinations of visual and proprioceptive perturbations (i.e., error), including an instance when the directions of perturbation in both stimuli opposed each another. In the subsequent probe force-channel trial, the lateral force against the channel was quantified as the aftereffect to clarify the mechanism by which the motor adaptation system corrects movement in the event of visual and proprioceptive errors. We observed that the aftereffects had complex dependence on the visual and proprioceptive errors. Although this pattern could not be explained by previously proposed computational models based on the reliability of sensory information, we found that it could be reasonably explained by a mechanism known as divisive normalization, which was the reported mechanism underlying the integration of multisensory signals in neurons. Furthermore, we discovered evidence that the motor memory for each sensory modality developed separately in accordance with a divisive normalization mechanism and that the outputs of both memories were integrated. These results provide a novel view of the utilization and integration of different sensory modality signals in motor adaptation.SIGNIFICANCE STATEMENT The mechanism of utilization of multimodal sensory information by the motor control system to perform limb movements with accuracy is a fundamental question. However, the mechanism of integration of these different sensory modalities for movement control and learning remains highly debatable. Herein, we demonstrate that multisensory integration in the motor learning system can be reasonably explained by divisive normalization, a canonical computation, ubiquitously observed in the brain (Carandini and Heeger, 2011). Moreover, we provide evidence of a novel idea that integration does not occur at the sensory information processing level, but at the motor execution level, after the motor memory for each sensory modality is separately created.
Copyright © 2020 the authors.

Entities:  

Keywords:  divisive normalization; motor learning; multisensory integration; reaching movement

Year:  2020        PMID: 31924610      PMCID: PMC7044737          DOI: 10.1523/JNEUROSCI.1745-19.2019

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  54 in total

1.  Learning of visuomotor transformations for vectorial planning of reaching trajectories.

Authors:  J W Krakauer; Z M Pine; M F Ghilardi; C Ghez
Journal:  J Neurosci       Date:  2000-12-01       Impact factor: 6.167

2.  Persistence of motor adaptation during constrained, multi-joint, arm movements.

Authors:  R A Scheidt; D J Reinkensmeyer; M A Conditt; W Z Rymer; F A Mussa-Ivaldi
Journal:  J Neurophysiol       Date:  2000-08       Impact factor: 2.714

3.  Multimodal representation of limb endpoint position in the posterior parietal cortex.

Authors:  Ying Shi; Gregory Apker; Christopher A Buneo
Journal:  J Neurophysiol       Date:  2013-01-23       Impact factor: 2.714

Review 4.  A Functional Taxonomy of Bottom-Up Sensory Feedback Processing for Motor Actions.

Authors:  Stephen H Scott
Journal:  Trends Neurosci       Date:  2016-07-01       Impact factor: 13.837

5.  Motor learning relies on integrated sensory inputs in ADHD, but over-selectively on proprioception in autism spectrum conditions.

Authors:  Jun Izawa; Sarah E Pekny; Mollie K Marko; Courtney C Haswell; Reza Shadmehr; Stewart H Mostofsky
Journal:  Autism Res       Date:  2012-02-22       Impact factor: 5.216

6.  A Neural Signature of Divisive Normalization at the Level of Multisensory Integration in Primate Cortex.

Authors:  Tomokazu Ohshiro; Dora E Angelaki; Gregory C DeAngelis
Journal:  Neuron       Date:  2017-07-19       Impact factor: 17.173

Review 7.  Normalization as a canonical neural computation.

Authors:  Matteo Carandini; David J Heeger
Journal:  Nat Rev Neurosci       Date:  2011-11-23       Impact factor: 34.870

8.  A normalization model of multisensory integration.

Authors:  Tomokazu Ohshiro; Dora E Angelaki; Gregory C DeAngelis
Journal:  Nat Neurosci       Date:  2011-05-08       Impact factor: 24.884

9.  State Estimation for Early Feedback Responses in Reaching: Intramodal or Multimodal?

Authors:  Leonie Oostwoud Wijdenes; W Pieter Medendorp
Journal:  Front Integr Neurosci       Date:  2017-12-19

10.  Visual feedback is not necessary for the learning of novel dynamics.

Authors:  David W Franklin; Udell So; Etienne Burdet; Mitsuo Kawato
Journal:  PLoS One       Date:  2007-12-19       Impact factor: 3.240

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  5 in total

1.  The effect of visual uncertainty on implicit motor adaptation.

Authors:  Jonathan S Tsay; Guy Avraham; Hyosub E Kim; Darius E Parvin; Zixuan Wang; Richard B Ivry
Journal:  J Neurophysiol       Date:  2020-11-25       Impact factor: 2.714

2.  Understanding implicit sensorimotor adaptation as a process of proprioceptive re-alignment.

Authors:  Jonathan S Tsay; Hyosub Kim; Adrian M Haith; Richard B Ivry
Journal:  Elife       Date:  2022-08-15       Impact factor: 8.713

Review 3.  The Psychology of Reaching: Action Selection, Movement Implementation, and Sensorimotor Learning.

Authors:  Hyosub E Kim; Guy Avraham; Richard B Ivry
Journal:  Annu Rev Psychol       Date:  2020-09-25       Impact factor: 24.137

4.  Interactions between sensory prediction error and task error during implicit motor learning.

Authors:  Jonathan S Tsay; Adrian M Haith; Richard B Ivry; Hyosub E Kim
Journal:  PLoS Comput Biol       Date:  2022-03-23       Impact factor: 4.779

5.  Changes in error-correction behavior according to visuomotor maps in goal-directed projection tasks.

Authors:  Ayane Kusafuka; Ryoji Onagawa; Arata Kimura; Kazutoshi Kudo
Journal:  J Neurophysiol       Date:  2022-03-23       Impact factor: 2.714

  5 in total

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