Rahman, Bootan Mohammed (2017) Dynamics of neural systems with time delays. Post-Doctoral thesis (PhD), University of Sussex.

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Abstract

Complex networks are ubiquitous in nature. Numerous neurological diseases, such as
Alzheimer's, Parkinson's, epilepsy are caused by the abnormal collective behaviour of
neurons in the brain. In particular, there is a strong evidence that Parkinson's disease is
caused by the synchronisation of neurons, and understanding how and why such synchronisation
occurs will bring scientists closer to the design and implementation of appropriate
control to support desynchronisation required for the normal functioning of the brain. In
order to study the emergence of (de)synchronisation, it is necessary first to understand
how the dynamical behaviour of the system under consideration depends on the changes
in systems parameters. This can be done using a powerful mathematical method, called
bifurcation analysis, which allows one to identify and classify different dynamical regimes,
such as, for example, stable/unstable steady states, Hopf and fold bifurcations, and find
periodic solutions by varying parameters of the nonlinear system.
In real-world systems, interactions between elements do not happen instantaneously
due to a finite time of signal propagation, reaction times of individual elements, etc.
Moreover, time delays are normally non-constant and may vary with time. This means
that it is vital to introduce time delays in any realistic model of neural networks. In
this thesis, I consider four different models. First, in order to analyse the fundamental
properties of neural networks with time-delayed connections, I consider a system of four
coupled nonlinear delay differential equations. This model represents a neural network,
where one subsystem receives a delayed input from another subsystem. The exciting
feature of this model is the combination of both discrete and distributed time delays, where
distributed time delays represent the neural feedback between the two sub-systems, and the
discrete delays describe neural interactions within each of the two subsystems. Stability
properties are investigated for different commonly used distribution kernels, and the results
are compared to the corresponding stability results for networks with no distributed delays.
It is shown how approximations to the boundary of stability region of an equilibrium point
can be obtained analytically for the cases of delta, uniform, and gamma delay distributions.
Numerical techniques are used to investigate stability properties of the fully nonlinear
system and confirm our analytical findings.
In the second part of this thesis, I consider a globally coupled network composed of
active (oscillatory) and inactive (non-oscillatory) oscillators with distributed time delayed
coupling. Analytical conditions for the amplitude death, where the oscillations are quenched,
are obtained in terms of the coupling strength, the ratio of inactive oscillators, the width
of the uniformly distributed delay and the mean time delay for gamma distribution. The
results show that for uniform distribution, by increasing both the width of the delay distribution
and the ratio of inactive oscillators, the amplitude death region increases in the
mean time delay and the coupling strength parameter space. In the case of the gamma
distribution kernel, we find the amplitude death region in the space of the ratio of inactive
oscillators, the mean time delay for gamma distribution, and the coupling strength for
both weak and strong gamma distribution kernels.
Furthermore, I analyse a model of the subthalamic nucleus (STN)-globus palidus (GP)
network with three different transmission delays. A time-shift transformation reduces the
model to a system with two time delays, for which the existence of a unique steady
state is established. Conditions for stability of the steady state are derived in terms of
system parameters and the time delays. Numerical stability analysis is performed using
traceDDE and DDE-BIFTOOL in Matlab to investigate different dynamical regimes in
the STN-GP model, and to obtain critical stability boundaries separating stable (healthy)
and oscillatory (Parkinsonian-like) neural ring. Direct numerical simulations of the fully
nonlinear system are performed to confirm analytical findings, and to illustrate different
dynamical behaviours of the system.
Finally, I consider a ring of n neurons coupled through the discrete and distributed
time delays. I show that the amplitude death occurs in the symmetric (asymmetric) region
depending on the even (odd) number of neurons in the ring neural system. Analytical
conditions for linear stability of the trivial steady state are represented in a parameter space
of the synaptic weight of the self-feedback and the coupling strength between the connected
neurons, as well as in the space of the delayed self-feedback and the coupling strength
between the neurons. It is shown that both Hopf and steady-state bifurcations may occur
when the steady state loses its stability. Stability properties are also investigated for
different commonly used distribution kernels, such as delta function and weak gamma
distributions. Moreover, the obtained analytical results are confirmed by the numerical
simulations of the fully nonlinear system.

Item Type:	Thesis (Post-Doctoral)
Schools and Departments:	School of Mathematical and Physical Sciences > Mathematics
Subjects:	Q Science > QA Mathematics > QA0299 Analysis. Including analytical methods connected with physical problems Q Science > QP Physiology > QP0351 Neurophysiology and neuropsychology > QP0361 Nervous system
Depositing User:	Library Cataloguing
Date Deposited:	09 May 2017 11:30
Last Modified:	09 May 2017 11:30
URI:	http://srodev.sussex.ac.uk/id/eprint/67773

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