85 research outputs found
A gaussian process emulator for estimating the volume of tissue activated during deep brain stimulation
The volume of tissue activated (VTA) is a well-established approach to model the direct effects of deep brain stimulation (DBS) on neural tissue and previous studies have pointed to its potential clinical applications. However, the elevated computational time required to estimate the VTA with standard techniques used in biological neural modeling limits its suitability for practical use. The goal of this project was to develop
a novel methodology to reduce the computation time of VTA estimation. To that end, we built a Gaussian process emulator. It combines a field of multi-compartment axon models coupled to the stimulating electric field with a Gaussian process classifier (GPC); following the premise that computing the VTA from a field of axons is in essence a binary classification problem. We achieved a considerable reduction in the average
time required to estimate the VTA, under both ideal isotropic and realistic anisotropic brain tissue conductive
conditions, limiting the loss of accuracy and overcoming other drawbacks entailed by alternative methods
Desarrollo de un aplicativo para la visualización del volumen de tejido activo en estimulación cerebral profunda empleando un modelo no lineal de reducción de dimensionalidad
El Parkinson es una enfermedad con síntomas de desorden de movimiento, que puede ser tratada con la Estimulación Cerebral Profunda cuando los métodos convencionales con medicinas no son efectivos. Esta terapia implica la colocación de un electrodo en el ganglio basal, el tálamo, u otra estructura subcortical, el cual genera pulsos eléctricos de alta frecuencia en una región específica; el Volumen de Tejido Activo es la propagación espacial de la activación neuronal directa en respuesta a esta estimulación eléctrica, y es de suma importancia para los neurocirujanos contar con una herramienta que permita conocerlo; pero dado que la representación de estos volúmenes se realiza con estructuras de datos de alta dimensionalidad. es necesario trabajar con métodos de reducción de dimensionalidad para representar las estructuras en un espacio de características bidimensional. Para esto, este proyecto presenta, en primera instancia, un estudio para determinar el error de reconstrucción, obtenido con la medida de similitud Positive Matching Index, cuando se realiza el proceso de reducción sobre volúmenes simulados bajo condiciones isotrópicas y anisotrópicas, con las técnicas: Análisis de Componentes Principales y Análisis de Componentes Principales con Kernel. Y en segunda instancia, se desarrolló una solución informática que permite visualizar los volúmenes mientras se desplaza en un espacio de dos dimensiones. Finalmente se obtuvo, como resultados de investigación, los errores de reconstrucción en diferentes dimensiones, que permitieron determinar la eficiencia de estas técnicas; y una herramienta computacional que permite visualizar los volúmenes de tejido activo mientras se desplaza en un espacio bidimensional
Investigation into the mechanisms of depressive illness
Functional and structural brain abnormalities have been reported in many imaging
studies of depressive illness. However, the mechanisms by which these
abnormalities give rise to symptoms remain unknown. The work described in this
thesis focuses on such mechanisms, particularly with regard to neural predictive error
signals. Recently, these signals have been reported to be present in many studies on
animals and healthy humans. The central hypothesis explored in this thesis is that
depressive illness comprises a disorder of associative learning. Chapter 2 reviews
the brain regions frequently reported as abnormal in imaging studies of depressive
illness, and the normal function of these particular brain regions. It is concluded that
such regions comprise the neural substrate for associative learning and emotion.
However, confidence in this conclusion is limited by considerable variability in the
human imaging literature. Therefore, chapter 3 describes a meta-analysis, which
tests the hypothesis that, consistent with the non-imaging literature, the ventromedial
prefrontal cortex is most active during emotional experience. The results of the
meta-analysis were clearly consistent with this hypothesis. Chapter 4 provides an
introduction to neural predictive error signals from the general perspective of
homeostatic physiological regulation. Both experimental evidence supporting the
error signals, and various formal mathematical theories describing the error signals,
are summarised. This provides the background to chapter 5, which describes an
original fMRI study which tested the hypothesis that patients with depressive illness
would exhibit abnormal predictive error signals in response to unexpected
motivationally significant stimuli. Evidence of such abnormality was found.
Chapter 6 describes a further original study using transcranial ultrasound and
diffusion tensor imaging of the brainstem, which investigated reports of a subtle
structural abnormality in depressed patients. If present, it might give rise to
abnormal error signals. However, no structural abnormality was found. Finally,
chapter 7 discusses the significance of these findings in the context of clinical
features of depressive illness and a wide range of treatments, ranging from
psychotherapy through antidepressants to physical treatments. A number of potential
future studies are identified, which could clarify understanding of depressive illness
Towards a Brain-inspired Information Processing System: Modelling and Analysis of Synaptic Dynamics: Towards a Brain-inspired InformationProcessing System: Modelling and Analysis ofSynaptic Dynamics
Biological neural systems (BNS) in general and the central nervous system (CNS) specifically
exhibit a strikingly efficient computational power along with an extreme flexible and adaptive basis
for acquiring and integrating new knowledge. Acquiring more insights into the actual mechanisms
of information processing within the BNS and their computational capabilities is a core objective
of modern computer science, computational sciences and neuroscience. Among the main reasons
of this tendency to understand the brain is to help in improving the quality of life of people suffer
from loss (either partial or complete) of brain or spinal cord functions. Brain-computer-interfaces
(BCI), neural prostheses and other similar approaches are potential solutions either to help these
patients through therapy or to push the progress in rehabilitation. There is however a significant
lack of knowledge regarding the basic information processing within the CNS. Without a better
understanding of the fundamental operations or sequences leading to cognitive abilities, applications
like BCI or neural prostheses will keep struggling to find a proper and systematic way to
help patients in this regard. In order to have more insights into these basic information processing
methods, this thesis presents an approach that makes a formal distinction between the essence
of being intelligent (as for the brain) and the classical class of artificial intelligence, e.g. with
expert systems. This approach investigates the underlying mechanisms allowing the CNS to be
capable of performing a massive amount of computational tasks with a sustainable efficiency and
flexibility. This is the essence of being intelligent, i.e. being able to learn, adapt and to invent.
The approach used in the thesis at hands is based on the hypothesis that the brain or specifically a
biological neural circuitry in the CNS is a dynamic system (network) that features emergent capabilities.
These capabilities can be imported into spiking neural networks (SNN) by emulating the
dynamic neural system. Emulating the dynamic system requires simulating both the inner workings
of the system and the framework of performing the information processing tasks. Thus, this
work comprises two main parts. The first part is concerned with introducing a proper and a novel
dynamic synaptic model as a vital constitute of the inner workings of the dynamic neural system.
This model represents a balanced integration between the needed biophysical details and being
computationally inexpensive. Being a biophysical model is important to allow for the abilities of
the target dynamic system to be inherited, and being simple is needed to allow for further implementation
in large scale simulations and for hardware implementation in the future. Besides, the
energy related aspects of synaptic dynamics are studied and linked to the behaviour of the networks
seeking for stable states of activities. The second part of the thesis is consequently concerned with
importing the processing framework of the dynamic system into the environment of SNN. This
part of the study investigates the well established concept of binding by synchrony to solve the information binding problem and to proposes the concept of synchrony states within SNN. The
concepts of computing with states are extended to investigate a computational model that is based
on the finite-state machines and reservoir computing. Biological plausible validations of the introduced
model and frameworks are performed. Results and discussions of these validations indicate
that this study presents a significant advance on the way of empowering the knowledge about the
mechanisms underpinning the computational power of CNS. Furthermore it shows a roadmap on
how to adopt the biological computational capabilities in computation science in general and in
biologically-inspired spiking neural networks in specific. Large scale simulations and the development
of neuromorphic hardware are work-in-progress and future work. Among the applications
of the introduced work are neural prostheses and bionic automation systems
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 239, December 1982
This bibliography lists 318 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1982
Women in Science 2013
“Women in Science” summarizes research done by Smith College’s Summer Research Fellowship (SURF) Program participants. Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. In 2013, 167 students participated in SURF, supervised by 57 faculty mentor-advisors drawn from the Clark Science Center’s fourteen science, mathematics, and engineering departments and programs, and associated centers and units. At summer’s end, SURF participants were asked to summarize their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1000/thumbnail.jp
Investigation into the control of an upper-limb myoelectric prosthesis
SIGLEAvailable from British Library Document Supply Centre- DSC:DXN053608 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Aerospace Medicine and Biology: A cumulative index to a continuing bibliography
This publication is a cumulative index to the abstracts contained in Supplements 138 through 149 of AEROSPACE MEDICINE AND BIOLOGY: A CONTINUING BIBLIOGRAPHY. It includes three indexes -- subject, personal author, and corporate source
- …