A neural surveyor to map touch on the body

Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and global positioning systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

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A neural surveyor in somatosensory cortex

10.1101/2020.06.26.173419 ◽

2020 ◽

Author(s):

Luke E. Miller ◽

Cécile Fabio ◽

Rob van Beers ◽

Alessandro Farnè ◽

W. Pieter Medendorp

Keyword(s):

Somatosensory Cortex ◽

Somatosensory System ◽

Afferent Input ◽

Feedforward Neural Network ◽

The Body ◽

Tuning Curves ◽

Tactile Localization ◽

Neural Signature ◽

Sensory Map ◽

Area 3B

SummaryPerhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Though it seems straightforward, this simple representation belies the complex link between an activation in somatosensory Area 3b and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, though how this is computed by neural networks is unknown. We propose that somatosensory cortex implements multilateration, a common computation used by surveying and GPS systems to localize objects. Specifically, to decode touch location on the body, the somatosensory system estimates the relative distance between the afferent input and the body’s joints. We show that a simple feedforward neural network which captures the receptive field properties of somatosensory cortex implements a Bayes-optimal multilateral decoder via a combination of bell-shaped (Area 3b) and sigmoidal (Areas 1/2) tuning curves. Simulations demonstrated that this decoder produced a unique pattern of localization variability between two joints that was not produced by other known neural decoders. Finally, we identify this neural signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

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Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽

10.3390/s21030798 ◽

2021 ◽

Vol 21 (3) ◽

pp. 798

Author(s):

Hamed Darbandi ◽

Filipe Serra Bragança ◽

Berend Jan van der Zwaag ◽

John Voskamp ◽

Annik Imogen Gmel ◽

...

Keyword(s):

Machine Learning ◽

The Body ◽

Biomechanical Analysis ◽

Estimation Accuracy ◽

Speed Estimation ◽

Motion Patterns ◽

Positioning Systems ◽

Machine Learning Approach ◽

Global Positioning ◽

Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

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Corticostriatal transformations in the primate somatosensory system. Projections from physiologically mapped body-part representations

Journal of Neurophysiology ◽

10.1152/jn.1991.66.4.1249 ◽

1991 ◽

Vol 66 (4) ◽

pp. 1249-1263 ◽

Cited By ~ 193

Author(s):

A. W. Flaherty ◽

A. M. Graybiel

Keyword(s):

Basal Ganglia ◽

Receptive Fields ◽

Somatosensory System ◽

Body Part ◽

The Body ◽

Saimiri Sciureus ◽

Body Parts ◽

Cortical Areas ◽

Somatosensory Information ◽

Multiple Regions

1. The basal ganglia of primates receive somatosensory input carried largely by corticostriatal fibers. To determine whether map-transformations occur in this corticostriatal system, we investigated how electrophysiologically defined regions of the primary somatosensory cortex (SI) project to the striatum in the squirrel monkey (Saimiri sciureus). Receptive fields in the hand, mouth, and foot representations of cortical areas 3a, 3b, and 1 were mapped by multiunit recording; and small volumes of distinguishable anterograde tracers were injected into different body-part representations in single SI areas. 2. Analysis of labeled projections established that at least four types of systematic remapping occur in the primate corticostriatal system. 1) An area of cortex representing a single body part sends fibers that diverge to innervate multiple regions in the putamen, forming branching, patchy fields that are densest in the lateral putamen. The fields do not form elongated cylindrical forms; rather, they are nearly as extended mediolaterally as they are rostrocaudally. 2) Cortical regions representing hand, mouth, and foot send globally somatotopic, nonoverlapping projections to the putamen, but regions with closely related representations (such as those of the thumb and 5th finger in area 3b) send convergent, overlapping corticostriatal projections. The overlap is fairly precise in the caudal putamen, but in the rostral putamen the densest zones of the projections do not overlap. 3) Regions representing homologous body parts in different SI cortical areas send projections that converge in the putamen. This was true of paired projections from areas 3a and 3b, and from areas 3b and 1. Thus corticostriatal inputs representing distinct somatosensory submodalities can project to the same local regions within the striatum. Convergence is not always complete, however: in the rostral putamen of two cases comparing projections from areas 3a and 1, the densest zones of the projections did not overlap. 4) All projections from SI avoid striosomes and innervate discrete zones within the matrix. 3. These experiments demonstrate that the somatosensory representations of the body are reorganized as they are projected from SI to the somatosensory sector of the primate putamen. This remapping suggests that the striatal representation of the body may be functionally distinct from that of each area of SI. The patchy projections may provide a basis for redistribution of somatosensory information to discrete output systems in the basal ganglia. Transformations in the corticostriatal system could thus be designed for modulating different movement-related programs.

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Complex Organization of Human Primary Motor Cortex: A High-Resolution fMRI Study

Journal of Neurophysiology ◽

10.1152/jn.90531.2008 ◽

2008 ◽

Vol 100 (4) ◽

pp. 1800-1812 ◽

Cited By ~ 160

Author(s):

Jeffrey D. Meier ◽

Tyson N. Aflalo ◽

Sabine Kastner ◽

Michael S. A. Graziano

Keyword(s):

High Resolution ◽

Motor Cortex ◽

Primary Motor Cortex ◽

Body Part ◽

The Body ◽

Body Parts ◽

Human Motor Cortex ◽

Fmri Study ◽

Complex Organization ◽

Somatotopic Map

A traditional view of the human motor cortex is that it contains an overlapping sequence of body part representations from the tongue in a ventral location to the foot in a dorsal location. In this study, high-resolution functional MRI (1.5 × 1.5 × 2 mm) was used to examine the somatotopic map in the lateral motor cortex of humans, to determine whether it followed the traditional somatotopic order or whether it contained any violations of that somatotopic order. The arm and hand representation had a complex organization in which the arm was relatively emphasized in two areas: one dorsal and the other ventral to a region that emphasized the fingers. This violation of a traditional somatotopic order suggests that the motor cortex is not merely a map of the body but is topographically shaped by other influences, perhaps including correlations in the use of body parts in the motor repertoire.

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Confiabilidad de los registros obtenidos por medio de dos sistemas de posicionamiento global en actividades de desplazamiento humano

Cuerpo Cultura y Movimiento ◽

10.15332/s2248-4418.2012.03-4.07 ◽

2012 ◽

Vol 2 (3 - 4) ◽

pp. 117

Author(s):

Jeison Daniel Salazar Pachón ◽

David Armando Chaparro Obando ◽

Nicolás Tordi

Keyword(s):

Global Positioning Systems ◽

Positioning Systems ◽

Global Positioning ◽

El Sistema

El presente estudio examinó la confiabilidad de los registros de dos sistemas de posicionamiento global (global positioning systems [GPS]), Garmin310XT y FRWDB600, sobre las distancias recorridas a diferentes velocidades, tras un protocolo a pie y otro en bicicleta realizados en una pista atlética. Esta información se comparó con el trayecto real de recorrido, hecho a partir del cálculo: ritmo de recorrido (r) = distancia recorrida (d) x tiempo de recorri- do, y se controló con un metrónomo Sport Beeper. Los participantes fueron dos jóvenes de edad media 22 años ± 1, activos físicamente. En los resultados, se observaron diferencias entre los registros de ambos sistemas GPS; el protocolo a pie Garmin tuvo un porcentaje de concordancia de 101,1%, mientras que FRWD presentó 103%. En el protocolo en bicicleta se obtuvo 103,4% y 101,6%, respectivamente. Se concluyó que el uso de GPS es más fiable cuando las velocidades de desplazamiento humano son bajas o moderadas para el sistema Garmin (7-14 km/h), ya que al ser más altas la fiabilidad de la información podría ser menor, mientras que el sistema FRWD presentó mayor confiabilidad en velocidades moderadas (14-22 km/h).

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Confiabilidad de los registros obtenidos por medio de dos sistemas de posicionamiento global en actividades de desplazamiento humano

Cuerpo Cultura y Movimiento ◽

10.15332/s2248-4418.2011.0002.06 ◽

2011 ◽

Vol 1 (2) ◽

pp. 117

Author(s):

Jeison Daniel Salazar Pachón ◽

David Armando Chaparro Obando ◽

Nicolás Tordi

Keyword(s):

Global Positioning Systems ◽

Positioning Systems ◽

Global Positioning ◽

El Sistema

El presente estudio examinó la confiabilidad de los registros de dos sistemas de posicionamiento global (global positioning systems [GPS]), Garmin310XT y FRWDB600, sobre las distancias recorridas a diferentes velocidades, tras un protocolo a pie y otro en bicicleta realizados en una pista atlética. Esta información se comparó con el trayecto real de recorrido, hecho a partir del cálculo: ritmo de recorrido (r) = distancia recorrida (d) x tiempo de recorrido, y se controló con un metrónomo Sport Beeper. Los participantes fueron dos jóvenes de edad media 22 años ± 1, activos físicamente. En los resultados, se observaron diferencias entre los registros de ambos sistemas GPS; el protocolo a pie Garmin tuvo un porcentaje de concordancia de 101,1%, mientras que FRWD presentó 103%. En el protocolo en bicicleta se obtuvo 103,4% y 101,6%, respectivamente. Se concluyó que el uso de GPS es más fiable cuando las velocidades de desplazamiento humano son bajas o mo- deradas para el sistema Garmin (7-14 km/h), ya que al ser más altas la fiabilidad de la información podría ser menor, mientras que el sistema FRWD presentó mayor confiabilidad en velocidades moderadas (14-22 km/h).

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