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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa.de:bsz:15-qucosa-82128 |
| Date | 12 January 2012 |
| Creators | El-Laithy, Karim |
| Contributors | Universität Leipzig, Fakultät für Mathematik und Informatik, Prof. Dr. Martin Bogdan, Prof. Dr. Martin Bogdan, Prof. Dr. Julius Georgiou |
| Publisher | Universitätsbibliothek Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf |
Page generated in 0.0022 seconds