40 research outputs found
Information theoretic approach to tactile encoding and discrimination
The human sense of touch integrates feedback from a multitude of touch receptors, but
how this information is represented in the neural responses such that it can be extracted
quickly and reliably is still largely an open question. At the same time, dexterous
robots equipped with touch sensors are becoming more common, necessitating better
methods for representing sequentially updated information and new control strategies
that aid in extracting relevant features for object manipulation from the data. This
thesis uses information theoretic methods for two main aims: First, the neural code
for tactile processing in humans is analyzed with respect to how much information is
transmitted about tactile features. Second, machine learning approaches are used in
order to influence both what data is gathered by a robot and how it is represented by
maximizing information theoretic quantities.
The first part of this thesis contains an information theoretic analysis of data recorded
from primary tactile neurons in the human peripheral somatosensory system. We examine
the differences in information content of two coding schemes, namely spike
timing and spike counts, along with their spatial and temporal characteristics. It is
found that estimates of the neurons’ information content based on the precise timing
of spikes are considerably larger than for spikes counts. Moreover, the information
estimated based on the timing of the very first elicited spike is at least as high as
that provided by spike counts, but in many cases considerably higher. This suggests
that first spike latencies can serve as a powerful mechanism to transmit information
quickly. However, in natural object manipulation tasks, different tactile impressions
follow each other quickly, so we asked whether the hysteretic properties of the human
fingertip affect neural responses and information transmission. We find that past
stimuli affect both the precise timing of spikes and spike counts of peripheral tactile
neurons, resulting in increased neural noise and decreased information about ongoing
stimuli. Interestingly, the first spike latencies of a subset of afferents convey information
primarily about past stimulation, hinting at a mechanism to resolve ambiguity
resulting from mechanical skin properties.
The second part of this thesis focuses on using machine learning approaches in a
robotics context in order to influence both what data is gathered and how it is represented
by maximizing information theoretic quantities. During robotic object manipulation,
often not all relevant object features are known, but have to be acquired
from sensor data. Touch is an inherently active process and the question arises of how to best control the robot’s movements so as to maximize incoming information about
the features of interest. To this end, we develop a framework that uses active learning
to help with the sequential gathering of data samples by finding highly informative
actions. The viability of this approach is demonstrated on a robotic hand-arm setup,
where the task involves shaking bottles of different liquids in order to determine the
liquid’s viscosity from tactile feedback only. The shaking frequency and the rotation
angle of shaking are optimized online. Additionally, we consider the problem of how
to better represent complex probability distributions that are sequentially updated, as
approaches for minimizing uncertainty depend on an accurate representation of that
uncertainty. A mixture of Gaussians representation is proposed and optimized using
a deterministic sampling approach. We show how our method improves on similar
approaches and demonstrate its usefulness in active learning scenarios.
The results presented in this thesis highlight how information theory can provide a
principled approach for both investigating how much information is contained in sensory
data and suggesting ways for optimization, either by using better representations
or actively influencing the environment
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Multiplexing Stimulus Information through Rate and Temporal Codes in Primate Somatosensory Cortex
Our ability to perceive and discriminate textures relies on the transduction and processing of complex, high-frequency vibrations elicited in the fingertip as it is scanned across a surface. How naturalistic vibrations, and by extension texture, are encoded in the responses of neurons in primary somatosensory cortex (S1) is unknown. Combining single unit recordings in awake macaques and perceptual judgments obtained from human subjects, we show that vibratory amplitude is encoded in the strength of the response evoked in S1 neurons. In contrast, the frequency composition of the vibrations, up to 800 Hz, is not encoded in neuronal firing rates, but rather in the phase-locked responses of a subpopulation of neurons. Moreover, analysis of perceptual judgments suggests that spike timing not only conveys stimulus information but also shapes tactile perception. We conclude that information about the amplitude and frequency of natural vibrations is multiplexed at different time scales in S1, and encoded in the rate and temporal patterning of the response, respectively.</p
Information about Complex Fingertip Parameters in Individual Human Tactile Afferent Neurons
Although information in tactile afferent neurons represented by firing rates has been studied extensively over nearly a century, recent
studies suggest that precise spike timing might be more important than firing rates. Here, we used information theory to compare the
information content in the discharges of 92 tactile afferents distributed over the entire terminal segment of the fingertip when it was
contacted by surfaces with different curvatures and force directions representative of everyday manipulations. Estimates of the information
content with regard to curvature and force direction based on the precise timing of spikes were at least 2.2 times and 1.6 times,
respectively, larger than that of spike counts during a 125 ms period of force increase. Moreover, the information regarding force
direction based on the timing of the very first elicited spike was comparable with that provided by spike counts and more than twice as
large with respect to object shape. For all encoding schemes, afferents terminating close to the stimulation site tended to convey more
information about surface curvature than more remote afferents that tended to convey more information about force direction. Finally,
coding schemes based on spike timing and spike counts overall contributed mostly independent information. We conclude that information
about tactile stimuli in timing of spikes in primary afferents, even if limited to the first spikes, surpasses that contained in firing
rates and that these measures of afferents’ responses might capture different aspects of the stimulus
Active Estimation of Object Dynamics Parameters with Tactile Sensors
The estimation of parameters that affect the
dynamics of objects—such as viscosity or internal degrees of
freedom—is an important step in autonomous and dexterous
robotic manipulation of objects. However, accurate and efficient
estimation of these object parameters may be challenging due
to complex, highly nonlinear underlying physical processes. To
improve on the quality of otherwise hand-crafted solutions,
automatic generation of control strategies can be helpful.
We present a framework that uses active learning to help
with sequential gathering of data samples, using informationtheoretic
criteria to find the optimal actions to perform at each
time step. We demonstrate the usefulness of our approach on a
robotic hand-arm setup, where the task involves shaking bottles
of different liquids in order to determine the liquid’s viscosity
from only tactile feedback. We optimize the shaking frequency
and the rotation angle of shaking in an online manner in order
to speed up convergence of estimates
Edge orientation signals in tactile afferents of macaques
The orientation of edges indented into the skin has been shown to be encoded in the responses of neurons in primary somatosensory cortex in a manner that draws remarkable analogies to their counterparts in primary visual cortex. According to the classical view, orientation tuning arises from the integration of untuned input from thalamic neurons with aligned but spatially displaced receptive fields (RFs). In a recent microneurography study with human subjects, the precise temporal structure of the responses of individual mechanoreceptive afferents to scanned edges was found to carry information about their orientation. This putative mechanism could in principle contribute to or complement the classical rate-based code for orientation. In the present study, we further examine orientation information carried by mechanoreceptive afferents of Rhesus monkeys. To this end, we record the activity evoked in cutaneous mechanoreceptive afferents when edges are indented into or scanned across the skin. First, we confirm that information about the edge orientation can be extracted from the temporal patterning in afferent responses of monkeys, as is the case in humans. Second, we find that while the coarse temporal profile of the response can be predicted linearly from the layout of the RF, the fine temporal profile cannot. Finally, we show that orientation signals in tactile afferents are often highly dependent on stimulus features other than orientation, which complicates putative decoding strategies. We discuss the challenges associated with establishing a neural code at the somatosensory periphery, where afferents are exquisitely sensitive and nearly deterministic
Fundamental studies on dynamic wear behavior of SBR rubber compounds modified by SBR rubber powder
The aim of this study is focused on the experimental investigation of dynamic wear behavior of carbon black filled rubber compounds comprising pristine styrene butadiene rubber (SBR) together with incorporated SBR ground rubber (rubber powder). We also analyzed and described quantitatively the service conditions of some dynamically loaded rubber products, which are liable to wear (e.g. conveyor belts, tires). Beside the well-known standard test method to characterize wear resistance at steady-state conditions, we used an own developed testing equipment based on gravimetric determination of mass loss of rubber test specimen to investigate the influence of rubber powder content on dynamic wear depending on varying impact energy levels. Incorporation of SBR rubber powder in SBR rubber compounds increases wear. With increasing rubber powder content the wear at steady-state conditions progressively increases. However, the level of wear at dynamic loading conditions increases only once, but stays constant subsequently even with contents of incorporated rubber powder
Malleability of the cortical hand map following a finger nerve block
Electrophysiological studies in monkeys show that finger amputation triggers local remapping within the deprived primary somatosensory cortex (S1). Human neuroimaging research, however, shows persistent S1 representation of the missing hand\u27s fingers, even decades after amputation. Here, we explore whether this apparent contradiction stems from underestimating the distributed peripheral and central representation of fingers in the hand map. Using pharmacological single-finger nerve block and 7-tesla neuroimaging, we first replicated previous accounts (electrophysiological and other) of local S1 remapping. Local blocking also triggered activity changes to nonblocked fingers across the entire hand area. Using methods exploiting interfinger representational overlap, however, we also show that the blocked finger representation remained persistent despite input loss. Computational modeling suggests that both local stability and global reorganization are driven by distributed processing underlying the topographic map, combined with homeostatic mechanisms. Our findings reveal complex interfinger representational features that play a key role in brain (re)organization, beyond (re)mapping