scholarly journals A modular neural network model of grasp movement generation

2019 ◽  
Author(s):  
Jonathan A. Michaels ◽  
Stefan Schaffelhofer ◽  
Andres Agudelo-Toro ◽  
Hansjörg Scherberger
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Neural Data ◽  
Network Training ◽  
Muscle Dynamics ◽  
Lesion Studies ◽  
Object Feature ◽  
Hand Area

SummaryOne of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and using visual features to generate the required muscle dynamics to grasp objects would explain the neural and computational basis of the grasping circuit. Modular networks with object feature input and sparse inter-module connectivity outperformed other models at explaining neural data and the inter-area relationships present in the biological circuit, despite the absence of neural data during network training. Network dynamics were governed by simple rules, and targeted lesioning of modules produced deficits similar to those observed in lesion studies, providing a potential explanation for how grasping movements are generated.

scholarly journals A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping

2020 ◽  
Vol 117 (50) ◽  
pp. 32124-32135 ◽  
Author(s):  
Jonathan A. Michaels ◽  
Stefan Schaffelhofer ◽  
Andres Agudelo-Toro ◽  
Hansjörg Scherberger
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Primary Motor Cortex ◽  
Initial Conditions ◽  
Premotor Cortex ◽  
Brain Regions ◽  
Network Activity ◽  
Visually Guided ◽  
Neural Data ◽  
Muscle Dynamics

One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. However, no comprehensive model exists that links all steps of processing from vision to action. We hypothesized that a recurrent neural network mimicking the modular structure of the anatomical circuit and trained to use visual features of objects to generate the required muscle dynamics used by primates to grasp objects would give insight into the computations of the grasping circuit. Internal activity of modular networks trained with these constraints strongly resembled neural activity recorded from the grasping circuit during grasping and paralleled the similarities between brain regions. Network activity during the different phases of the task could be explained by linear dynamics for maintaining a distributed movement plan across the network in the absence of visual stimulus and then generating the required muscle kinematics based on these initial conditions in a module-specific way. These modular models also outperformed alternative models at explaining neural data, despite the absence of neural data during training, suggesting that the inputs, outputs, and architectural constraints imposed were sufficient for recapitulating processing in the grasping circuit. Finally, targeted lesioning of modules produced deficits similar to those observed in lesion studies of the grasping circuit, providing a potential model for how brain regions may coordinate during the visually guided grasping of objects.

scholarly journals Selective Regions of the Visuomotor System Are Related to Gain-Induced Changes in Force Error

2010 ◽  
Vol 103 (4) ◽  
pp. 2114-2123 ◽  
Author(s):  
Stephen A. Coombes ◽  
Daniel M. Corcos ◽  
Lisa Sprute ◽  
David E. Vaillancourt
Keyword(s):  
Visual Feedback ◽  
Visual Information ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Substantial Reduction ◽  
Behavioral Effects ◽  
Visual Gain ◽  
Visuomotor System ◽  
Left And Right ◽  
Induced Changes

When humans perform movements and receive on-line visual feedback about their performance, the spatial qualities of the visual information alter performance. The spatial qualities of visual information can be altered via the manipulation of visual gain and changes in visual gain lead to changes in force error. The current study used functional magnetic resonance imaging during a steady-state precision grip force task to examine how cortical and subcortical brain activity can change with visual gain induced changes in force error. Small increases in visual gain <1° were associated with a substantial reduction in force error and a small increase in the spatial amplitude of visual feedback. These behavioral effects corresponded with an increase in activation bilaterally in V3 and V5 and in left primary motor cortex and left ventral premotor cortex. Large increases in visual gain >1° were associated with a small change in force error and a large change in the spatial amplitude of visual feedback. These behavioral effects corresponded with increased activity bilaterally in dorsal and ventral premotor areas and right inferior parietal lobule. Finally, activity in the left and right lobule VI of the cerebellum and left and right putamen did not change with increases in visual gain. Together, these findings demonstrate that the visuomotor system does not respond uniformly to changes in the gain of visual feedback. Instead, specific regions of the visuomotor system selectively change in activity related to large changes in force error and large changes in the spatial amplitude of visual feedback.

Simultaneous Recording of Macaque Premotor and Primary Motor Cortex Neuronal Populations Reveals Different Functional Contributions to Visuomotor Grasp

2007 ◽  
Vol 98 (1) ◽  
pp. 488-501 ◽  
Author(s):  
M. A. Umilta ◽  
T. Brochier ◽  
R. L. Spinks ◽  
R. N. Lemon
Keyword(s):  
Motor Cortex ◽  
Visual Information ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Visual Presentation ◽  
Simultaneous Recording ◽  
Visually Guided ◽  
Reach To Grasp ◽  
Neuronal Populations ◽  
Different Types

To understand the relative contributions of primary motor cortex (M1) and area F5 of the ventral premotor cortex (PMv) to visually guided grasp, we made simultaneous multiple electrode recordings from the hand representations of these two areas in two adult macaque monkeys. The monkeys were trained to fixate, reach out and grasp one of six objects presented in a pseudorandom order. In M1 326 task-related neurons, 104 of which were identified as pyramidal tract neurons, and 138 F5 neurons were analyzed as separate populations. All three populations showed activity that distinguished the six objects grasped by the monkey. These three populations responded in a manner that generalized across different sets of objects. F5 neurons showed object/grasp related tuning earlier than M1 neurons in the visual presentation and premovement periods. Also F5 neurons generally showed a greater preference for particular objects/grasps than did M1 neurons. F5 neurons remained tuned to a particular grasp throughout both the premovement and reach-to-grasp phases of the task, whereas M1 neurons showed different selectivity during the different phases. We also found that different types of grasp appear to be represented by different overall levels of activity within the F5-M1 circuit. Altogether these properties are consistent with the notion that F5 grasping-related neurons play a role in translating visual information about the physical properties of an object into the motor commands that are appropriate for grasping, and which are elaborated within M1 for delivery to the appropriate spinal machinery controlling hand and digit muscles.

scholarly journals Mapping Human Laryngeal Motor Cortex during Vocalization

2020 ◽  
Vol 30 (12) ◽  
pp. 6254-6269 ◽  
Author(s):  
Nicole Eichert ◽  
Daniel Papp ◽  
Rogier B Mars ◽  
Kate E Watkins
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Neural Representation ◽  
Functional Magnetic Resonance ◽  
Human Larynx ◽  
Motor Representations ◽  
Quantitative Neuroimaging ◽  
Hand Area

Abstract The representations of the articulators involved in human speech production are organized somatotopically in primary motor cortex. The neural representation of the larynx, however, remains debated. Both a dorsal and a ventral larynx representation have been previously described. It is unknown, however, whether both representations are located in primary motor cortex. Here, we mapped the motor representations of the human larynx using functional magnetic resonance imaging and characterized the cortical microstructure underlying the activated regions. We isolated brain activity related to laryngeal activity during vocalization while controlling for breathing. We also mapped the articulators (the lips and tongue) and the hand area. We found two separate activations during vocalization—a dorsal and a ventral larynx representation. Structural and quantitative neuroimaging revealed that myelin content and cortical thickness underlying the dorsal, but not the ventral larynx representation, are similar to those of other primary motor representations. This finding confirms that the dorsal larynx representation is located in primary motor cortex and that the ventral one is not. We further speculate that the location of the ventral larynx representation is in premotor cortex, as seen in other primates. It remains unclear, however, whether and how these two representations differentially contribute to laryngeal motor control.

scholarly journals Mapping human laryngeal motor cortex during vocalization

2020 ◽  
Author(s):  
Nicole Eichert ◽  
Daniel Papp ◽  
Rogier B. Mars ◽  
Kate E. Watkins
Keyword(s):  
Motor Control ◽  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Neural Representation ◽  
Human Larynx ◽  
Motor Representations ◽  
Quantitative Neuroimaging ◽  
Hand Area

AbstractThe representations of the articulators involved in human speech production are organized somatotopically in primary motor cortex. The neural representation of the larynx, however, remains debated. Both a dorsal and a ventral larynx representation have been previously described. It is unknown, however, whether both representations are located in primary motor cortex. Here, we mapped the motor representations of the human larynx using fMRI and characterized the cortical microstructure underlying the activated regions. We isolated brain activity related to laryngeal activity during vocalization while controlling for breathing. We also mapped the articulators (the lips and tongue) and the hand area. We found two separate activations during vocalization – a dorsal and a ventral larynx representation. Structural and quantitative neuroimaging revealed that myelin content and cortical thickness underlying the dorsal, but not the ventral larynx representation, are similar to those of other primary motor representations. This finding confirms that the dorsal larynx representation is located in primary motor cortex and that the ventral one is not. We further speculate that the location of the ventral larynx representation is in premotor cortex, as seen in other primates. It remains unclear, however, whether and how these two representations differentially contribute to laryngeal motor control.

Reorganization of Remote Cortical Regions After Ischemic Brain Injury: A Potential Substrate for Stroke Recovery

2003 ◽  
Vol 89 (6) ◽  
pp. 3205-3214 ◽  
Author(s):  
S. B. Frost ◽  
S. Barbay ◽  
K. M. Friel ◽  
E. J. Plautz ◽  
R. J. Nudo
Keyword(s):  
Motor Cortex ◽  
Primary Motor Cortex ◽  
Motor Behavior ◽  
Premotor Cortex ◽  
Critical Role ◽  
Retrieval Task ◽  
Gradual Improvement ◽  
Intact Tissue ◽  
Ventral Premotor Cortex ◽  
Hand Area

Although recent neurological research has shed light on the brain's mechanisms of self-repair after stroke, the role that intact tissue plays in recovery is still obscure. To explore these mechanisms further, we used microelectrode stimulation techniques to examine functional remodeling in cerebral cortex after an ischemic infarct in the hand representation of primary motor cortex in five adult squirrel monkeys. Hand preference and the motor skill of both hands were assessed periodically on a pellet retrieval task for 3 mo postinfarct. Initial postinfarct motor impairment of the contralateral hand was evident in each animal, followed by a gradual improvement in performance over 1–3 mo. Intracortical microstimulation mapping at 12 wk after infarct revealed substantial enlargements of the hand representation in a remote cortical area, the ventral premotor cortex. Increases ranged from 7.2 to 53.8% relative to the preinfarct ventral premotor hand area, with a mean increase of 36.0 ± 20.8%. This enlargement was proportional to the amount of hand representation destroyed in primary motor cortex. That is, greater sparing of the M1 hand area resulted in less expansion of the ventral premotor cortex hand area. These results suggest that neurophysiologic reorganization of remote cortical areas occurs in response to cortical injury and that the greater the damage to reciprocal intracortical pathways, the greater the plasticity in intact areas. Reorganization in intact tissue may provide a neural substrate for adaptive motor behavior and play a critical role in postinjury recovery of function.

The frontal longitudinal system as revealed through the fiber microdissection technique: structural evidence underpinning the direct connectivity of the prefrontal-premotor circuitry

2020 ◽  
Vol 133 (5) ◽  
pp. 1503-1515 ◽  
Author(s):  
Spyridon Komaitis ◽  
Aristotelis V. Kalyvas ◽  
Georgios P. Skandalakis ◽  
Evangelos Drosos ◽  
Evgenia Lani ◽  
...  
Keyword(s):  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Frontal Area ◽  
Frontal Pole ◽  
Fiber System ◽  
Dorsolateral Prefrontal ◽  
Anterior Extension ◽  
Brodmann Areas ◽  
Longitudinal Fiber ◽  
Long Fibers

OBJECTIVEThe purpose of this study was to investigate the morphology, connectivity, and correlative anatomy of the longitudinal group of fibers residing in the frontal area, which resemble the anterior extension of the superior longitudinal fasciculus (SLF) and were previously described as the frontal longitudinal system (FLS).METHODSFifteen normal adult formalin-fixed cerebral hemispheres collected from cadavers were studied using the Klingler microdissection technique. Lateral to medial dissections were performed in a stepwise fashion starting from the frontal area and extending to the temporoparietal regions.RESULTSThe FLS was consistently identified as a fiber pathway residing just under the superficial U-fibers of the middle frontal gyrus or middle frontal sulcus (when present) and extending as far as the frontal pole. The authors were able to record two different configurations: one consisting of two distinct, parallel, longitudinal fiber chains (13% of cases), and the other consisting of a single stem of fibers (87% of cases). The fiber chains’ cortical terminations in the frontal and prefrontal area were also traced. More specifically, the FLS was always recorded to terminate in Brodmann areas 6, 46, 45, and 10 (premotor cortex, dorsolateral prefrontal cortex, pars triangularis, and frontal pole, respectively), whereas terminations in Brodmann areas 4 (primary motor cortex), 47 (pars orbitalis), and 9 were also encountered in some specimens. In relation to the SLF system, the FLS represented its anterior continuation in the majority of the hemispheres, whereas in a few cases it was recorded as a completely distinct tract. Interestingly, the FLS comprised shorter fibers that were recorded to interconnect exclusively frontal areas, thus exhibiting different fiber architecture when compared to the long fibers forming the SLF.CONCLUSIONSThe current study provides consistent, focused, and robust evidence on the morphology, architecture, and correlative anatomy of the FLS. This fiber system participates in the axonal connectivity of the prefrontal-premotor cortices and allegedly subserves cognitive-motor functions. Based in the SLF hypersegmentation concept that has been advocated by previous authors, the FLS should be approached as a distinct frontal segment within the superior longitudinal system.

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

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