scholarly journals A functional cortical network for sensorimotor sequence generation

2019 ◽  
Author(s):  
Duo Xu ◽  
Yuxi Chen ◽  
Angel M. Delgado ◽  
Natasha C. Hughes ◽  
Mingyuan Dong ◽  
...  
Keyword(s):  
Sensory Feedback ◽  
Closed Loop ◽  
Hierarchical Control ◽  
Tactile Feedback ◽  
Cortical Network ◽  
Sequence Generation ◽  
Cortical Areas ◽  
Complex Sequences ◽  
The Brain ◽  
Sequence Execution

The brain generates complex sequences of movements that can be flexibly reconfigured in real-time based on sensory feedback, but how this occurs is not fully understood. We developed a novel ‘sequence licking’ task in which mice directed their tongue to a target that moved through a series of locations. Mice could rapidly reconfigure the sequence online based on tactile feedback. Closed-loop optogenetics and electrophysiology revealed that tongue/jaw regions of somatosensory (S1TJ) and motor (M1TJ) cortex encoded and controlled tongue kinematics at the level of individual licks. Tongue premotor (anterolateral motor, ALM) cortex encoded intended tongue angle in a smooth manner that spanned individual licks and even whole sequences, and progress toward the reward that marked successful sequence execution. ALM activity regulated sequence initiation, but multiple cortical areas collectively controlled termination of licking. Our results define a functional cortical network for hierarchical control of sensory- and reward-guided orofacial sequence generation.

scholarly journals A clinical experience with a hierarchically controlled myoelectric hand prosthesis with vibro-tactile feedback

1993 ◽  
Vol 17 (1) ◽  
pp. 56-64 ◽  
Author(s):  
P. J. Kyberd ◽  
N. Mustapha ◽  
F. Carnegie ◽  
P. H. Chappell
Keyword(s):  
Sensory Feedback ◽  
Grip Force ◽  
Outcome Measure ◽  
Control Method ◽  
Hierarchical Control ◽  
Tactile Feedback ◽  
Ease Of Use ◽  
Direct Control ◽  
Improved Performance ◽  
Electronic Controller

Improved performance of externally powered myoelectric hands is possible when the direct control of the digit flexion and grip force are given over to an electronic controller which frees the operator to concentrate on other demands. Design: A commercial myoelectric hand was modified to take the new touch and slip sensors and novel control method. Subject: An adult male with a traumatic mid-forearm amputation. Outcome measure: The range and ease of use of the prosthetics system. Result: The hand was easily and usefully operated in the home and work environment. Conclusion: Hierarchical control of a hand is possible using sensory feedback to a sophisticated electronic controller. Such a control method reduces the demands on the user's concentration and enhances the hand's range.

scholarly journals Augmenting Quadriplegic Hand Function Using a Sensorimotor Demultiplexing Neural Interface

2019 ◽  
Author(s):  
PD Ganzer ◽  
SC Colachis ◽  
MA Schwemmer ◽  
DA Friedenberg ◽  
CE Swiftney ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Sensory Feedback ◽  
Closed Loop ◽  
Primary Motor Cortex ◽  
Brain Computer Interface ◽  
Sensory Information ◽  
Feedback System ◽  
Computer Interface ◽  
Sense Of Touch ◽  
The Brain

AbstractBackgroundThe sense of touch is a key component of motor function. Severe spinal cord injury (SCI) should essentially eliminate sensory information transmission to the brain, that originates from skin innervated from below the lesion. We assessed the hypothesis that, following SCI, residual hand sensory information is transmitted to the brain, can be decoded amongst competing sensorimotor signals, and used to enhance the sense of touch via an intracortically controlled closed-loop brain-computer interface (BCI) system.MethodsExperiments were performed with a participant who has an AIS-A C5 SCI and an intracortical recording array implanted in left primary motor cortex (M1). Sensory stimulation and standard clinical sensorimotor functional assessments were used throughout a series of several mechanistic experiments.FindingsOur results demonstrate that residual afferent hand sensory signals surprisingly reach human primary motor cortex and can be simultaneously demultiplexed from ongoing efferent motor intention, enabling closed-loop sensory feedback during brain-computer interface (BCI) operation. The closed-loop sensory feedback system was able to detect residual sensory signals from up to the C8 spinal level. Using the closed-loop sensory feedback system enabled significantly enhanced object touch detection, sense of agency, movement speed, and other sensorimotor functions.InterpretationTo our knowledge, this is the first demonstration of simultaneously decoding multiplexed afferent and efferent activity from human cortex to control multiple assistive devices, constituting a ‘sensorimotor demultiplexing’ BCI. Overall, our results support the hypothesis that sub-perceptual neural signals can be decoded reliably and transformed to conscious perception, significantly augmenting function.FundingInternal funding was provided for this study from Battelle Memorial Institute and The Ohio State University Center for Neuromodulation.

Human Cerebral Potentials During Motor Training Under Different Forms of Sensory Feedback

1999 ◽  
Vol 13 (4) ◽  
pp. 234-244
Author(s):  
Uwe Niederberger ◽  
Wolf-Dieter Gerber
Keyword(s):  
Healthy Subjects ◽  
Sensory Feedback ◽  
Motor Task ◽  
Tactile Feedback ◽  
Proprioceptive Feedback ◽  
Motor Training ◽  
Feedback Group ◽  
Emg Feedback ◽  
The Right ◽  
Training Success

Abstract In two experiments with four and two groups of healthy subjects, a novel motor task, the voluntary abduction of the right big toe, was trained. This task cannot usually be performed without training and is therefore ideal for the study of elementary motor learning. A systematic variation of proprioceptive, tactile, visual, and EMG feedback was used. In addition to peripheral measurements such as the voluntary range of motion and EMG output during training, a three-channel EEG was recorded over Cz, C3, and C4. The movement-related brain potential during distinct periods of the training was analyzed as a central nervous parameter of the ongoing learning process. In experiment I, we randomized four groups of 12 subjects each (group P: proprioceptive feedback; group PT: proprioceptive and tactile feedback; group PTV: proprioceptive, tactile, and visual feedback; group PTEMG: proprioceptive, tactile, and EMG feedback). Best training results were reported from the PTEMG and PTV groups. The movement-preceding cortical activity, in the form of the amplitude of the readiness potential at the time of EMG onset, was greatest in these two groups. Results of experiment II revealed a similar effect, with a greater training success and a higher electrocortical activation under additional EMG feedback compared to proprioceptive feedback alone. Sensory EMG feedback as evaluated by peripheral and central nervous measurements appears to be useful in motor training and neuromuscular re-education.

scholarly journals The evolution of hierarchical structure building capacity for language and music: a bottom-up perspective

Primates ◽  
2021 ◽  
Author(s):  
Rie Asano
Keyword(s):  
Hierarchical Structure ◽  
Hierarchical Control ◽  
Bottom Up ◽  
Building Capacity ◽  
Cognitive Domains ◽  
Structure Building ◽  
Language And Music ◽  
Directed Action ◽  
Brain Architecture ◽  
The Brain

AbstractA central property of human language is its hierarchical structure. Humans can flexibly combine elements to build a hierarchical structure expressing rich semantics. A hierarchical structure is also considered as playing a key role in many other human cognitive domains. In music, auditory-motor events are combined into hierarchical pitch and/or rhythm structure expressing affect. How did such a hierarchical structure building capacity evolve? This paper investigates this question from a bottom-up perspective based on a set of action-related components as a shared basis underlying cognitive capacities of nonhuman primates and humans. Especially, I argue that the evolution of hierarchical structure building capacity for language and music is tractable for comparative evolutionary study once we focus on the gradual elaboration of shared brain architecture: the cortico-basal ganglia-thalamocortical circuits for hierarchical control of goal-directed action and the dorsal pathways for hierarchical internal models. I suggest that this gradual elaboration of the action-related brain architecture in the context of vocal control and tool-making went hand in hand with amplification of working memory, and made the brain ready for hierarchical structure building in language and music.

scholarly journals The emulation theory of representation: Motor control, imagery, and perception

2004 ◽  
Vol 27 (3) ◽  
pp. 377-396 ◽  
Author(s):  
Rick Grush
Keyword(s):  
Motor Control ◽  
Sensory Feedback ◽  
Sensory Input ◽  
Neural Circuits ◽  
Sensory Information ◽  
The Body ◽  
Feedback Delay ◽  
Run Off ◽  
Effects Of Feedback ◽  
The Brain

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

scholarly journals The Effect of Oral Tactile Sensitivity on Texture Perception and Mastication Behavior in Humans

2018 ◽  
Author(s):  
Grace E. Shupe ◽  
Arran Wilson ◽  
Curtis R. Luckett
Keyword(s):  
High Sensitivity ◽  
Tactile Feedback ◽  
Interindividual Variation ◽  
Texture Perception ◽  
Tactile Acuity ◽  
Force Sensitivity ◽  
Significant Difference ◽  
Low Sensitivity ◽  
Oral Sensitivity ◽  
The Brain

AbstractMastication behavior is a notable source of interindividual variation in texture perception and could be linked to oral sensitivity. As oral sensitivity declines so does the amount of tactile feedback relayed to the brain, resulting in less effective manipulation or food and a reduced ability to discriminate differences. To address these hypotheses, we measured masticatory behavior and related this to texture discrimination and oral sensitivity. The study was performed on 41 participants in two groups, with high (n = 20) or low (n=21) sensitivity. Oral sensitivity was measured using a battery of tests that included: oral stereognosis, lingual tactile acuity, and bite force sensitivity. Sensitivity to texture changes was measured using a series of triangle tests with confectionaries of different hardness, with masticatory patterns and behaviors being video recorded and analyzed using jaw tracking software. Overall, there was no significant difference between high and low sensitivity participants and their ability to distinguish texture changes. But, there were significantly different trends found between the groups based on their masticatory behaviors including chewing pattern and overall number of chews. But, it was found that multiple masticatory behaviors were being modulated by oral sensitivity, including overall chewing cycles used (p < 0.0001). More, specifically those in the high sensitivity group used more stochastic chewing movements, while those in the low sensitivity group were found to use crescent-shaped chewing cycles. It was also noted that in the high sensitivity group the jaw moved further distances (p < 0.0001) in all phases and moved at a higher velocity when opening (p < 0.0001) but not when closing, when compared to the low sensitivity group. These results help bolster evidence that mastication and oral sensitivity are related.

Hierarchical Dynamical Model for Multiple Cortical Neural Decoding

2021 ◽  
Vol 33 (5) ◽  
pp. 1372-1401
Author(s):  
Xi Liu ◽  
Xiang Shen ◽  
Shuhang Chen ◽  
Xiang Zhang ◽  
Yifan Huang ◽  
...  
Keyword(s):  
Hierarchical Structure ◽  
Primary Motor Cortex ◽  
Neural Pathway ◽  
Neural Recordings ◽  
Cortical Areas ◽  
The Hierarchical Structure ◽  
Brain States ◽  
Heavy Tailed ◽  
The Brain

Abstract Motor brain machine interfaces (BMIs) interpret neural activities from motor-related cortical areas in the brain into movement commands to control a prosthesis. As the subject adapts to control the neural prosthesis, the medial prefrontal cortex (mPFC), upstream of the primary motor cortex (M1), is heavily involved in reward-guided motor learning. Thus, considering mPFC and M1 functionality within a hierarchical structure could potentially improve the effectiveness of BMI decoding while subjects are learning. The commonly used Kalman decoding method with only one simple state model may not be able to represent the multiple brain states that evolve over time as well as along the neural pathway. In addition, the performance of Kalman decoders degenerates in heavy-tailed nongaussian noise, which is usually generated due to the nonlinear neural system or influences of movement-related noise in online neural recording. In this letter, we propose a hierarchical model to represent the brain states from multiple cortical areas that evolve along the neural pathway. We then introduce correntropy theory into the hierarchical structure to address the heavy-tailed noise existing in neural recordings. We test the proposed algorithm on in vivo recordings collected from the mPFC and M1 of two rats when the subjects were learning to perform a lever-pressing task. Compared with the classic Kalman filter, our results demonstrate better movement decoding performance due to the hierarchical structure that integrates the past failed trial information over multisite recording and the combination with correntropy criterion to deal with noisy heavy-tailed neural recordings.

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