67 research outputs found
Peripheral mechanisms for fine tactile perception: behavioural and modelling approach
The tactile system is highly complex. The properties of its central and peripheral components determine the way external stimuli are transformed into perception. At the very first stage, first-order tactile neurons respond to skin mechanical deformation and their activation convey a representation of the sensed object (i.e., encoding). However, there are several open questions regarding which factors can significantly influence the peripheral neural response and hence, perception. The goal of the work presented in this thesis is to provide new evidence about the link between skin properties, object’s characteristics, first-order tactile neurons response and discriminative judgments.
Chapter One provides an overview of the tactile system with a focus on the peripheral components (e.g., skin, first-order tactile neurons), as well as a summary of the relevant behavioural findings on tactile perception in Young and Elderly. Chapter Two outlines the methods used in this work including psychophysics, a device to present tactile stimuli in a controlled fashion, skin measurement techniques, and manufacturing of fine-textured stimuli. Chapter Three provides an in-depth review of computational models that simulate the response of first-order neurons and how they can be applied for psychophysical research. Chapter Four is the first empirical chapter that evaluates the effects of skin and mechanoreceptive afferent properties on spatial tactile sensitivity in young and elderly participants assessed with the 2-point discrimination task. Chapter 5 is the second empirical chapter that investigates the effects of the interaction between finger and surface properties on the detection sensitivity for a single microdot in young participants with active exploration.
Chapter Six summarises the findings of the research undertaken in my doctoral studies and discusses their implications for understanding the sensory mechanisms underlying tactile perception. Overall, the findings presented in this thesis suggest that the progressive loss of mechanoafferent units contribute to the decline in tactile spatial acuity as predicted by a population model of the afferent response, and provide new evidence on the complex effects of frictional changes and the role of skin biomechanics on the detection of a microdot
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
Vestibulospinal circuit in the larval zebrafish
The vestibular system sense gravity and self-motion to help animals maintain body balance. Although vestibular signals inform the brain of the directions and speed of our body movements, it still remains unclear how these sensory information are processed and organized in the central nervous system. My thesis aims to illustrate neural computation underlying central vestibular tuning and the topographic organization of the vestibular circuits. First I established a novel approach to perform whole-cell recording of synaptic inputs in vivo during multi-axis movements in the central vestibular neurons. This technical advance allowed me to simultaneously measure presynaptic and postsynaptic tuning, along with the presynaptic convergence pattern and synaptic strengths, all at the larval zebrafish vestibulospinal nucleus. I showed that convergence of inputs with dissimilar sensory responses can create complex postsynaptic tuning, whereas convergence of inputs with similar responses mediates simpler postsynaptic tuning. This direct demonstration of how simple and complex vestibular tuning are computed centrally, resolved a major gap in the vestibular field between theoretical prediction and experimental evidence. Next, I used serial-section electron microscopy to reconstruct a high- resolution ultrastructure of the entire vestibular peripheral circuit. I mapped the connectivity of all 91 vestibular hair cells and 105 afferents in one utricle and traced afferent projections to the vestibular brainstem. This work reveals the first known topographic map organized by both sensory tuning and developmental age in the vestibular ganglion. It also shows that the early born and late born peripheral pathways coincide with two vestibular streams encoding the phasic and tonic signals, respectively. Together my study suggests that vestibular circuits from the peripheral sensors to the central neurons are potentially organized by development and movement speed
Biomechatronics: Harmonizing Mechatronic Systems with Human Beings
This eBook provides a comprehensive treatise on modern biomechatronic systems
centred around human applications. A particular emphasis is given to exoskeleton
designs for assistance and training with advanced interfaces in human-machine
interaction. Some of these designs are validated with experimental results which
the reader will find very informative as building-blocks for designing such systems.
This eBook will be ideally suited to those researching in biomechatronic area with
bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design
at post-graduate level
Neuroplasticity of Ipsilateral Cortical Motor Representations, Training Effects and Role in Stroke Recovery
This thesis examines the contribution of the ipsilateral hemisphere to motor control with the aim of evaluating the potential of the contralesional hemisphere to contribute to motor recovery after stroke. Predictive algorithms based on neurobiological principles emphasize integrity of the ipsilesional corticospinal tract as the strongest prognostic indicator of good motor recovery. In contrast, extensive lesions placing reliance on alternative contralesional ipsilateral motor pathways are associated with poor recovery. Within the predictive algorithms are elements of motor control that rely on contributions from ipsilateral motor pathways, suggesting that balanced, parallel contralesional contributions can be beneficial. Current therapeutic approaches have focussed on the maladaptive potential of the contralesional hemisphere and sought to inhibit its activity with neuromodulation. Using Transcranial Magnetic Stimulation I seek examples of beneficial plasticity in ipsilateral cortical motor representations of expert performers, who have accumulated vast amounts of deliberate practise training skilled bilateral activation of muscles habitually under ipsilateral control. I demonstrate that ipsilateral cortical motor representations reorganize in response to training to acquisition of skilled motor performance. Features of this reorganization are compatible with evidence suggesting ipsilateral importance in synergy representations, controlled through corticoreticulopropriospinal pathways. I demonstrate that ipsilateral plasticity can associate positively with motor recovery after stroke. Features of plastic change in ipsilateral cortical representations are shown in response to robotic training of chronic stroke patients. These findings have implications for the individualization of motor rehabilitation after stroke, and prompt reappraisal of the approach to therapeutic intervention in the chronic phase of stroke
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