Literature DB >> 30503617

Closed-Loop Control of Active Sensing Movements Regulates Sensory Slip.

Debojyoti Biswas1, Luke A Arend2, Sarah A Stamper3, Balázs P Vágvölgyi2, Eric S Fortune4, Noah J Cowan5.   

Abstract

Active sensing involves the production of motor signals for the purpose of acquiring sensory information [1-3]. The most common form of active sensing, found across animal taxa and behaviors, involves the generation of movements-e.g., whisking [4-6], touching [7, 8], sniffing [9, 10], and eye movements [11]. Active sensing movements profoundly affect the information carried by sensory feedback pathways [12-15] and are modulated by both top-down goals (e.g., measuring weight versus texture [1, 16]) and bottom-up stimuli (e.g., lights on or off [12]), but it remains unclear whether and how these movements are controlled in relation to the ongoing feedback they generate. To investigate the control of movements for active sensing, we created an experimental apparatus for freely swimming weakly electric fish, Eigenmannia virescens, that modulates the gain of reafferent feedback by adjusting the position of a refuge based on real-time videographic measurements of fish position. We discovered that fish robustly regulate sensory slip via closed-loop control of active sensing movements. Specifically, as fish performed the task of maintaining position inside the refuge [17-22], they dramatically up- or downregulated fore-aft active sensing movements in relation to a 4-fold change of experimentally modulated reafferent gain. These changes in swimming movements served to maintain a constant magnitude of sensory slip. The magnitude of sensory slip depended on the presence or absence of visual cues. These results indicate that fish use two controllers: one that controls the acquisition of information by regulating feedback from active sensing movements and another that maintains position in the refuge, a control structure that may be ubiquitous in animals [23, 24].
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Eigenmannia virescens; active sensing; augmented reality; closed loop; control theory; gymnotiformes; reafferent feedback

Mesh:

Year:  2018        PMID: 30503617     DOI: 10.1016/j.cub.2018.11.002

Source DB:  PubMed          Journal:  Curr Biol        ISSN: 0960-9822            Impact factor:   10.834


  9 in total

1.  Gain control in the sensorimotor system.

Authors:  Eiman Azim; Kazuhiko Seki
Journal:  Curr Opin Physiol       Date:  2019-03-22

2.  Learning active sensing strategies using a sensory brain-machine interface.

Authors:  Andrew G Richardson; Yohannes Ghenbot; Xilin Liu; Han Hao; Cole Rinehart; Sam DeLuccia; Solymar Torres Maldonado; Gregory Boyek; Milin Zhang; Firooz Aflatouni; Jan Van der Spiegel; Timothy H Lucas
Journal:  Proc Natl Acad Sci U S A       Date:  2019-08-13       Impact factor: 11.205

3.  Task-Related Sensorimotor Adjustments Increase the Sensory Range in Electrolocation.

Authors:  Federico Pedraja; Volker Hofmann; Julie Goulet; Jacob Engelmann
Journal:  J Neurosci       Date:  2019-12-09       Impact factor: 6.167

4.  Tuning movement for sensing in an uncertain world.

Authors:  Chen Chen; Todd D Murphey; Malcolm A MacIver
Journal:  Elife       Date:  2020-09-22       Impact factor: 8.140

5.  Novel Functions of Feedback in Electrosensory Processing.

Authors:  Volker Hofmann; Maurice J Chacron
Journal:  Front Integr Neurosci       Date:  2019-09-13

6.  Idiosyncratic selection of active touch for shape perception.

Authors:  Ehud Ahissar; Amos Arieli; Neomi Mizrachi; Guy Nelinger
Journal:  Sci Rep       Date:  2022-02-21       Impact factor: 4.379

7.  Distance estimation from monocular cues in an ethological visuomotor task.

Authors:  Philip R L Parker; Elliott T T Abe; Natalie T Beatie; Emmalyn S P Leonard; Dylan M Martins; Shelby L Sharp; David G Wyrick; Luca Mazzucato; Cristopher M Niell
Journal:  Elife       Date:  2022-09-20       Impact factor: 8.713

8.  Variability in locomotor dynamics reveals the critical role of feedback in task control.

Authors:  Eric S Fortune; Noah J Cowan; Ismail Uyanik; Shahin Sefati; Sarah A Stamper; Kyoung-A Cho; M Mert Ankarali
Journal:  Elife       Date:  2020-01-23       Impact factor: 8.140

9.  Closed loop motor-sensory dynamics in human vision.

Authors:  Liron Zipora Gruber; Ehud Ahissar
Journal:  PLoS One       Date:  2020-10-15       Impact factor: 3.240

  9 in total

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