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

2016 ◽  
Vol 39 (8) ◽  
pp. 512-526 ◽  
Author(s):  
Stephen H. Scott
Keyword(s):  
Sensory Feedback ◽  
Bottom Up ◽  
Feedback Processing ◽  
Motor Actions

scholarly journals Plastic Changes in Hand Proprioception Following Force-Field Motor Learning

2010 ◽  
Vol 104 (3) ◽  
pp. 1213-1215 ◽  
Author(s):  
Daniel J. Goble ◽  
Joaquin A. Anguera
Keyword(s):  
Motor Learning ◽  
Force Field ◽  
Recent Work ◽  
Recent Article ◽  
Sensory Feedback ◽  
Motor Output ◽  
Feedback Processing ◽  
Plastic Changes ◽  
Proprioceptive Function ◽  
New Evidence

Motor neurophysiologists are placing greater emphasis on sensory feedback processing than ever before. In line with this shift, a recent article by Ostry and colleagues provided timely new evidence that force-field motor learning influences not only motor output, but also proprioceptive sense. In this Neuro Forum, the merits and limitations of Ostry and colleagues are explored in the context of recent work on proprioceptive function, including several recent studies from this journal.

scholarly journals Convergence of proprioceptive and visual feedback on neurons in primary motor cortex

2021 ◽  
Author(s):  
Kevin Patrick Cross ◽  
Douglas J Cook ◽  
Stephen H Scott
Keyword(s):  
Motor Cortex ◽  
Strong Convergence ◽  
Motor Function ◽  
Sensory Feedback ◽  
Single Neuron ◽  
Primary Motor Cortex ◽  
Related Activity ◽  
Feedback Processing ◽  
Types Of Information ◽  
Different Sources

An important aspect of motor function is our ability to rapidly generate goal-directed corrections for disturbances to the limb or behavioural goal. Primary motor cortex (M1) is a key region involved in feedback processing, yet we know little about how different sources of feedback are processed by M1. We examined feedback-related activity in M1 to compare how different sources (visual versus proprioceptive) and types of information (limb versus goal) are represented. We found sensory feedback had a broad influence on M1 activity with ~73% of neurons responding to at least one of the feedback sources. Information was also organized such that limb and goal feedback targeted the same neurons and evoked similar responses at the single-neuron and population levels indicating a strong convergence of feedback sources in M1.

scholarly journals Rotational dynamics in motor cortex are consistent with a feedback controller

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Hari Teja Kalidindi ◽  
Kevin P Cross ◽  
Timothy P Lillicrap ◽  
Mohsen Omrani ◽  
Egidio Falotico ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Recurrent Neural Networks ◽  
Sensory Feedback ◽  
Feedback Controller ◽  
Rotational Dynamics ◽  
Rotational Structure ◽  
Brain Areas ◽  
Neural Recordings ◽  
Neurophysiological Studies ◽  
Motor Actions

Recent studies have identified rotational dynamics in motor cortex (MC) which many assume arise from intrinsic connections in MC. However, behavioural and neurophysiological studies suggest that MC behaves like a feedback controller where continuous sensory feedback and interactions with other brain areas contribute substantially to MC processing. We investigated these apparently conflicting theories by building recurrent neural networks that controlled a model arm and received sensory feedback from the limb. Networks were trained to counteract perturbations to the limb and to reach towards spatial targets. Network activities and sensory feedback signals to the network exhibited rotational structure even when the recurrent connections were removed. Furthermore, neural recordings in monkeys performing similar tasks also exhibited rotational structure not only in MC but also in somatosensory cortex. Our results argue that rotational structure may also reflect dynamics throughout the voluntary motor system involved in online control of motor actions.

scholarly journals Ultra-Rapid Serial Visual Presentation Reveals Dynamics of Feedforward and Feedback Processes in the Ventral Visual Pathway

2018 ◽  
Author(s):  
Yalda Mohsenzadeh ◽  
Sheng Qin ◽  
Radoslaw M Cichy ◽  
Dimitrios Pantazis
Keyword(s):  
Rapid Serial Visual Presentation ◽  
Visual Recognition ◽  
Visual Presentation ◽  
Bottom Up ◽  
Feedback Processing ◽  
Recurrent Processing ◽  
Neuronal Processes ◽  
Early Visual Cortex ◽  
Multivariate Pattern ◽  
Activation Cascade

ABSTRACTHuman visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.

Sensorimotor Adaptation of Speech I

2002 ◽  
Vol 45 (2) ◽  
pp. 295-310 ◽  
Author(s):  
John F. Houde ◽  
Michael I. Jordan
Keyword(s):  
Auditory Feedback ◽  
Sensory Feedback ◽  
Control Experiment ◽  
Sensorimotor Adaptation ◽  
Auditory Speech ◽  
Masking Noise ◽  
Speech Feedback ◽  
Adaptation Experiment ◽  
Motor Actions ◽  
And Control

When motor actions (e.g., reaching with your hand) adapt to altered sensory feedback (e.g., viewing a shifted image of your hand through a prism), the phenomenon is called sensorimotor adaptation (SA). In the study reported here, SA was observed in speech. In two 2-hour experiments (adaptation and control), participants whispered a variety of CVC words. For those words containing the vowel /ε/, participants heard auditory feedback of their whispering. A DSP-based vocoder processed the participants' auditory feedback in real time, allowing the formant frequencies of participants' auditory speech feedback to be shifted. In the adaptation experiment, formants were shifted along one edge of the vowel triangle. For half the participants, formants were shifted so participants heard /a/ when they produced /ε/; for the other half, the shift made participants hear /i/ when they produced /ε/. During the adaptation experiment, participants altered their production of /ε/ to compensate for the altered feedback, and these production changes were retained when participants whispered with auditory feedback blocked by masking noise. In a control experiment, in which the formants were not shifted, participants' production changes were small and inconsistent. Participants exhibited a range of adaptations in response to the altered feedback, with some participants adapting almost completely, and other participants showing very little or no adaptation.

Laminar Computing by Cerebral Cortex

2021 ◽  
pp. 353-369
Author(s):  
Stephen Grossberg
Keyword(s):  
Cerebral Cortex ◽  
Adaptive Algorithms ◽  
General Purpose ◽  
Cortical Layers ◽  
Top Down ◽  
Bottom Up ◽  
Feedback Processing ◽  
Laminar Organization ◽  
Intelligent Behavior ◽  
Language And Cognition

The cerebral cortex computes the highest forms of biological intelligence in all sensory and cognitive modalities. Neocortical cells are organized into circuits that form six cortical layers in all cortical areas that carry out perception and cognition. Variations in cell properties within these layers and their connections have been used to classify the cerebral cortex into more than fifty divisions, or areas, to which distinct functions have been attributed. Why the cortex has a laminar organization for the control of behavior has, however, remained a mystery until recently. Also mysterious has been how variations on this ubiquitous laminar cortical design can give rise to so many different types of intelligent behavior. This chapter explains how Laminar Computing contributes to biological intelligence, and how layered circuits of neocortical cells support all the various kinds of higher-order biological intelligence, including vision, language, and cognition, using variations of the same canonical laminar circuit. This canonical circuit can be used in general-purpose VLSI chips that can be specialized to carry out different kinds of biological intelligence, and seamlessly joined together to control autonomous adaptive algorithms and mobile robots. These circuits show how preattentive automatic bottom-up processing and attentive task-selective top-down processing are joined together in the deeper cortical layers to form a decision interface. Here, bottom-up and top-down constraints cooperate and compete to generate the best decisions, by combining properties of fast feedforward and feedback processing, analog and digital computing, and preattentive and attentive learning, including laminar ART properties such as analog coherence.

scholarly journals Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Yalda Mohsenzadeh ◽  
Sheng Qin ◽  
Radoslaw M Cichy ◽  
Dimitrios Pantazis
Keyword(s):  
Rapid Serial Visual Presentation ◽  
Visual Recognition ◽  
Visual Presentation ◽  
Bottom Up ◽  
Feedback Processing ◽  
Recurrent Processing ◽  
Neuronal Processes ◽  
Early Visual Cortex ◽  
Multivariate Pattern ◽  
Activation Cascade

Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.

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