scholarly journals Computational Approaches to Spatial Orientation: From Transfer Functions to Dynamic Bayesian Inference

2008 ◽  
Vol 100 (6) ◽  
pp. 2981-2996 ◽  
Author(s):  
Paul R. MacNeilage ◽  
Narayan Ganesan ◽  
Dora E. Angelaki
Keyword(s):  
Bayesian Inference ◽  
Spatial Orientation ◽  
Particle Filtering ◽  
Transfer Functions ◽  
Linear Acceleration ◽  
Frequency Domain Analysis ◽  
Internal Models ◽  
Velocity Storage ◽  
Computational Approaches ◽  
The Brain

Spatial orientation is the sense of body orientation and self-motion relative to the stationary environment, fundamental to normal waking behavior and control of everyday motor actions including eye movements, postural control, and locomotion. The brain achieves spatial orientation by integrating visual, vestibular, and somatosensory signals. Over the past years, considerable progress has been made toward understanding how these signals are processed by the brain using multiple computational approaches that include frequency domain analysis, the concept of internal models, observer theory, Bayesian theory, and Kalman filtering. Here we put these approaches in context by examining the specific questions that can be addressed by each technique and some of the scientific insights that have resulted. We conclude with a recent application of particle filtering, a probabilistic simulation technique that aims to generate the most likely state estimates by incorporating internal models of sensor dynamics and physical laws and noise associated with sensory processing as well as prior knowledge or experience. In this framework, priors for low angular velocity and linear acceleration can explain the phenomena of velocity storage and frequency segregation, both of which have been modeled previously using arbitrary low-pass filtering. How Kalman and particle filters may be implemented by the brain is an emerging field. Unlike past neurophysiological research that has aimed to characterize mean responses of single neurons, investigations of dynamic Bayesian inference should attempt to characterize population activities that constitute probabilistic representations of sensory and prior information.

scholarly journals Perception of the dynamic visual vertical during sinusoidal linear motion

2017 ◽  
Vol 118 (4) ◽  
pp. 2499-2506 ◽  
Author(s):  
A. Pomante ◽  
L. P. J. Selen ◽  
W. P. Medendorp
Keyword(s):  
Vestibular System ◽  
Spatial Orientation ◽  
Linear Acceleration ◽  
The Body ◽  
Body Motion ◽  
Whole Body ◽  
Linear Motion ◽  
Modeling Framework ◽  
Visual Vertical ◽  
The Brain

The vestibular system provides information for spatial orientation. However, this information is ambiguous: because the otoliths sense the gravitoinertial force, they cannot distinguish gravitational and inertial components. As a consequence, prolonged linear acceleration of the head can be interpreted as tilt, referred to as the somatogravic effect. Previous modeling work suggests that the brain disambiguates the otolith signal according to the rules of Bayesian inference, combining noisy canal cues with the a priori assumption that prolonged linear accelerations are unlikely. Within this modeling framework the noise of the vestibular signals affects the dynamic characteristics of the tilt percept during linear whole-body motion. To test this prediction, we devised a novel paradigm to psychometrically characterize the dynamic visual vertical—as a proxy for the tilt percept—during passive sinusoidal linear motion along the interaural axis (0.33 Hz motion frequency, 1.75 m/s2peak acceleration, 80 cm displacement). While subjects ( n=10) kept fixation on a central body-fixed light, a line was briefly flashed (5 ms) at different phases of the motion, the orientation of which had to be judged relative to gravity. Consistent with the model’s prediction, subjects showed a phase-dependent modulation of the dynamic visual vertical, with a subject-specific phase shift with respect to the imposed acceleration signal. The magnitude of this modulation was smaller than predicted, suggesting a contribution of nonvestibular signals to the dynamic visual vertical. Despite their dampening effect, our findings may point to a link between the noise components in the vestibular system and the characteristics of dynamic visual vertical.NEW & NOTEWORTHY A fundamental question in neuroscience is how the brain processes vestibular signals to infer the orientation of the body and objects in space. We show that, under sinusoidal linear motion, systematic error patterns appear in the disambiguation of linear acceleration and spatial orientation. We discuss the dynamics of these illusory percepts in terms of a dynamic Bayesian model that combines uncertainty in the vestibular signals with priors based on the natural statistics of head motion.

scholarly journals Late Bayesian inference in sensorimotor behavior

2017 ◽  
Author(s):  
Evan Remington ◽  
Mehrdad Jazayeri
Keyword(s):  
Bayesian Inference ◽  
Human Subjects ◽  
Internal Models ◽  
Measurement Noise ◽  
Behavioral Variability ◽  
Sensorimotor Transformation ◽  
Bias Variance ◽  
Sensorimotor Skills ◽  
The Brain ◽  
Different Levels

AbstractSensorimotor skills rely on performing noisy sensorimotor computations on noisy sensory measurements. Bayesian models suggest that humans compensate for measurement noise and reduce behavioral variability by biasing perception toward prior expectations. Whether the same holds for noise in sensorimotor computations is not known. Testing human subjects in tasks with different levels of sensorimotor complexity, we found a similar bias-variance tradeoff associated with increased sensorimotor noise. This result was accurately captured by a model which implements Bayesian inference after – not before – sensorimotor transformation. These results indicate that humans perform “late inference” downstream of sensorimotor computations rather than, or in addition to, “early inference” in the perceptual domain. The brain thus possesses internal models of noise in both sensory measurements and sensorimotor computations.

scholarly journals Internal Models in Biological Control

2019 ◽  
Vol 2 (1) ◽  
pp. 339-364 ◽  
Author(s):  
Daniel McNamee ◽  
Daniel M. Wolpert
Keyword(s):  
Biological Control ◽  
Bayesian Inference ◽  
Feedback Control ◽  
Motor System ◽  
Sensorimotor Control ◽  
Internal Models ◽  
Optimal Feedback Control ◽  
Probabilistic Framework ◽  
Behavioral Evidence ◽  
The Brain

Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

A Multidimensional Model of the Effect of Gravity on the Spatial Orientation of the Monkey

1993 ◽  
Vol 3 (2) ◽  
pp. 141-161 ◽  
Author(s):  
Daniel M. Merfeld ◽  
Laurence R. Young ◽  
Charles M. Oman ◽  
Mark J. Shelhamert
Keyword(s):  
Spatial Orientation ◽  
Linear Acceleration ◽  
Vertical Axis ◽  
Velocity Storage ◽  
Sensory Conflict ◽  
Ocular Reflex ◽  
Vestibulo Ocular Reflex ◽  
The Difference ◽  
Primary Hypothesis ◽  
Conflict Information

A “sensory conflict” model of spatial orientation was developed. This mathematical model was based on concepts derived from observer theory, optimal observer theory, and the mathematical properties of coordinate rotations. The primary hypothesis is that the central nervous system of the squirrel monkey incorporates information about body dynamics and sensory dynamics to develop an internal model. The output of this central model (expected sensory afference) is compared to the actual sensory afference, with the difference defined as “sensory conflict”. The sensory conflict information is, in turn, used to drive central estimates of angular velocity (“velocity storage”), gravity (“gravity storage”), and linear acceleration (“acceleration storage”) toward more accurate values. The model successfully predicts “velocity storage” during rotation about an earth-vertical axis. The model also successfully predicts that the time constant of the horizontal vestibulo-ocular reflex is reduced and that the axis of eye rotation shifts toward alignment with gravity following postrotatory tilt. Finally, the model predicts the bias modulation, and decay components that have been observed during off-vertical axis rotations (OVAR).

scholarly journals A distributed, dynamic, parallel computational model: the role of noise in velocity storage

2012 ◽  
Vol 108 (2) ◽  
pp. 390-405 ◽  
Author(s):  
Faisal Karmali ◽  
Daniel M. Merfeld
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Time Constant ◽  
Particle Filtering ◽  
Imperfect Information ◽  
Storage Time ◽  
Velocity Storage ◽  
Body Parts ◽  
Observer Model ◽  
The Brain

Networks of neurons perform complex calculations using distributed, parallel computation, including dynamic “real-time” calculations required for motion control. The brain must combine sensory signals to estimate the motion of body parts using imperfect information from noisy neurons. Models and experiments suggest that the brain sometimes optimally minimizes the influence of noise, although it remains unclear when and precisely how neurons perform such optimal computations. To investigate, we created a model of velocity storage based on a relatively new technique–“particle filtering”–that is both distributed and parallel. It extends existing observer and Kalman filter models of vestibular processing by simulating the observer model many times in parallel with noise added. During simulation, the variance of the particles defining the estimator state is used to compute the particle filter gain. We applied our model to estimate one-dimensional angular velocity during yaw rotation, which yielded estimates for the velocity storage time constant, afferent noise, and perceptual noise that matched experimental data. We also found that the velocity storage time constant was Bayesian optimal by comparing the estimate of our particle filter with the estimate of the Kalman filter, which is optimal. The particle filter demonstrated a reduced velocity storage time constant when afferent noise increased, which mimics what is known about aminoglycoside ablation of semicircular canal hair cells. This model helps bridge the gap between parallel distributed neural computation and systems-level behavioral responses like the vestibuloocular response and perception.

The human brain: Chairman’s introduction

1981 ◽  
Vol 292 (1057) ◽  
pp. 151-153
Keyword(s):  
Human Brain ◽  
Cultural Evolution ◽  
Positive Feedback ◽  
Time Scale ◽  
Biological Evolution ◽  
Internal Models ◽  
Adaptive Behaviour ◽  
Myelin Sheaths ◽  
The Brain

Much has been said at the symposium about the pre-eminent role of the brain in the continuing emergence of man. Tobias has spoken of its explosive enlargement during the last 1 Ma, and how much of its enlargement in individual ontogeny is postnatal. We are born before our brains are fully grown and ‘wired up ’. During our long adolescence we build up internal models of the outside world and of the relations of parts of our bodies to it and to one another. Neurons that are present at birth spread their dendrites and project axons which acquire their myelin sheaths, and establish innumerable contacts with other neurons, over the years. New connections are formed; genetically endowed ones are stamped in or blanked off. People born without arms may grow up to use their toes in skills that are normally manual. Tobias, Darlington and others have stressed the enormous survival value of adaptive behaviour and the ‘positive feedback’ relation between biological and cultural evolution. The latter, the unique product of the unprecedentedly rapid biological evolution of big brains, advances on a time scale unknown to biological evolution.

Spatial orientation of the angular vestibulo-ocular reflex

1999 ◽  
Vol 9 (3) ◽  
pp. 163-172
Author(s):  
Bernard Cohen ◽  
Susan Wearne ◽  
Mingjia Dai ◽  
Theodore Raphan
Keyword(s):  
Spatial Orientation ◽  
Optokinetic Nystagmus ◽  
Vestibular Nuclei ◽  
Velocity Storage ◽  
Gravitational Acceleration ◽  
Ocular Reflex ◽  
Eye Rotation ◽  
Slow Velocity ◽  
Vestibulo Ocular Reflex ◽  
Vector Sum

During vestibular nystagmus, optokinetic nystagmus (OKN), and optokinetic afternystagmus (OKAN), the axis of eye rotation tends to align with the vector sum of linear accelerations acting on the head. This includes gravitational acceleration and the linear accelerations generated by translation and centrifugation. We define the summed vector of gravitational and linear accelerations as gravito-inertial acceleration (GIA) and designate the phenomenon of alignment as spatial orientation of the angular vestibuloocular reflex (aVOR). On the basis of studies in the monkey, we postulated that the spatial orientation of the aVOR is dependent on the slow (velocity storage) component of the aVOR, not on the short latency, compensatory aVOR component, which is in head-fixed coordinates. Experiments in which velocity storage was abolished by midline medullary section support this postulate. The velocity storage component of the aVOR is likely to be generated in the vestibular nuclei, and its spatial orientation was shown to be controlled through the nodulus and uvula of the vestibulo-cerebellum. Separate regions of the nodulus/uvula appear to affect the horizontal and vertical/torsional components of the response differently. Velocity storage is weaker in humans than in monkeys, but responds in a similar fashion in both species. We postulate that spatial orientation of the aVOR plays an important role in aligning gaze with the GIA and in maintaining balance during angular locomotion.

The Velocity Storage Mechanism of the Optokinetic Nystagmus Under Apparent Stimulus Movements in Squirrel Monkeys

1997 ◽  
Vol 7 (6) ◽  
pp. 441-451
Author(s):  
J. Kröller ◽  
F. Behrens ◽  
V.V. Marlinsky
Keyword(s):  
Steady State ◽  
Brain Stem ◽  
Apparent Movement ◽  
Squirrel Monkeys ◽  
Constant Light ◽  
Velocity Storage ◽  
Slow Phase ◽  
Stripe Pattern ◽  
Storage Mechanism ◽  
The Brain

Experiments in two awake untrained squirrel monkeys were performed to study the velocity storage mechanism during fast rise of OKN slow phase velocity. This was done by testing the monkey’s capability to perform OKN in response to a stationary-appearing stroboscopically illuminated stripe pattern of a horizontally rotating drum. Nystagmus was initially elicited during constant illumination lasting between 0.6 and 25 s. The periodicity of the stripe pattern was 2.37°. When after the constant light the flash illumination was switched on again, two types of behavior could occur, depending on the length of the constant light interval (CLI): 1) when the CLI was shorter than a threshold value of 6.2 seconds, the OKN ceased under the flash stimulation. Then a “post-OKN” occurred that increased with the length of the CLIs, indicating that the intermittently illuminated pattern did not provoke fixation suppression of OKN aftereffects. 2) when the CLI was above threshold, the OKN continued under the flash light: it will he called “apparent movement OKN.” The threshold CLI between the type 1 and the type 2 response did not depend on drum velocities between 21.5°/s and 71.3°/s. The average gain of the apparent movement OKN was 0.83 ± 0.04; gain and stability of slow phase eye movement velocity did not deviate systematically from the usually elicited OKN. OKAN after apparent movement OKN did not deviate from OKAN after constantly illuminated moving patterns. In response to the OKN initiation by a constantly illuminated pattern up to pattern velocities of 100°/s, the OKN steady state gain was reached within the first 2 or 3 nystagmus beats. We ascribe the increase of the post-OKN with CLI and the existence of a threshold constant light interval to activity-accumulation in the common velocity-to-position integrator (velocity storage) of the brain stem. Loading of the velocity storage takes place after the OKN gain has already reached the steady-state value. Apparent movement OKN could also be elicited in guinea pigs that lack an effective smooth pursuit system. We suggest that apparent movement OKN is produced by mechanisms located in the brain stem.

Spatial orientation of postrotatory nystagmus during static roll tilt in cats

2002 ◽  
Vol 12 (1) ◽  
pp. 15-23
Author(s):  
Keiko Yasuda ◽  
Hiroaki Fushiki ◽  
Rinnosuke Wada ◽  
Yukio Watanabe
Keyword(s):  
Spatial Orientation ◽  
Vertical Axis ◽  
Longitudinal Axis ◽  
Velocity Storage ◽  
Slow Phase ◽  
Storage Mechanism ◽  
Spatial Disorientation ◽  
Acceleration And Deceleration ◽  
Eye Velocity ◽  
Roll Tilt

While the stimulation of otolith inputs reduces the duration of postrotatory nystagmus (PRN), there is still room for dialogue about the effect of static tilt on the orientation of PRN. We studied one possible influence of static roll tilt on the spatial orientation of PRN in cats. The animal was rotated about an earth-vertical axis (EVA) at a constant velocity of 100 deg/s with an acceleration and deceleration of 120 deg / s 2 . Within two seconds after stopping EVA rotation, the animal was passively tilted at 45 deg/s about its longitudinal axis by as much as ± 90 deg in steps of 15 deg. Eye movements were measured with magnetic search coils. The angle of the PRN plane and its slow phase eye velocity were measured. The time constant of PRN decreased with an increase in roll tilt. The PRN plane remained earth horizontal within a range of ± 30 deg roll tilt. Beyond this range, the velocity of PRN decreased too rapidly to measure any change in orientation. Our results indicate a spatially limited and temporally short interaction of the semicircular canal and otolith signals in the velocity storage mechanism of cat PRN. Our data, along with previous studies, suggest that different species show different solutions to the problem of the imbalance and spatial disorientation during contradictory stimuli.

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