Neural synchrony in the brain at rest is usually variable and intermittent, thus intervals of predominantly synchronized activity are interrupted by intervals of desynchronized activity. Prior studies suggested that this temporal structure of the weakly synchronous activity might be functionally significant: many short desynchronizations may be functionally different from few long desynchronizations, even if the average synchrony level is the same. In this thesis, we use computational neuroscience methods to investigate the effects of (i) spike-timing dependent plasticity (STDP) and (ii) noise on the temporal patterns of synchronization in a simple model. The model is composed of two conductance-based neurons connected via excitatory unidirectional synapses. In (i) these excitatory synapses are made plastic, in (ii) two different types of noise implementation to model the stochasticity of membrane ion channels is considered. The plasticity results are taken from our recently published article, while the noise results are currently being compiled into a manuscript.<br><br>The dynamics of this network is subjected to the time-series analysis methods used in prior experimental studies. We provide numerical evidence that both STDP and channel noise can alter the synchronized dynamics in the network in several ways. This depends on the time scale that plasticity acts on and the intensity of the noise. However, in general, the action of STDP and noise in the simple network considered here is to promote dynamics with short desynchronizations (i.e. dynamics reminiscent of that observed in experimental studies) over dynamics with longer desynchronizations.

In wireless communication systems, the use of multiple antennas, also known as Multiple-Input Multiple-Output(MIMO) communications, is now a widely accepted and important technology for improving their reliability and throughput performance. However, in order to achieve the performance gains predicted by the theory, the transmitter and receiver need to have accurate and up-to-date Channel State Information(CSI) to overcome the vagaries of the fading environment. Traditionally, the CSI is obtained at the receiver by sending a known training sequence in the forward-link direction. This CSI has to be conveyed to the transmitter via a low-rate, low latency and noisy feedback channel in the reverse-link direction. This thesis addresses three key challenges in sending the CSI to the transmitter of a MIMO communication system over the reverse-link channel, and provides novel solutions to them. The first issue is that the available CSI at the receiver has to be quantized to a finite number of bits, sent over a noisy feedback channel, reconstructed at the transmitter, and used by the transmitter for precoding its data symbols. In particular, the CSI quantization technique has to be resilient to errors introduced by the noisy reverse-link channel, and it is of interest to design computationally simple, linear filters to mitigate these errors. The second issue addressed is the design of low latency and low decoding complexity error correction codes to provide protection against fading conditions and noise in the reverse-link channel. The third issue is to improve the resilience of the reverse-link channel to fading. The solution to the first problem is obtained by proposing two classes of receive filtering techniques, where the output of the source decoder is passed through a filter designed to reduce the overall distortion including the effect of the channel noise. This work combines the high resolution quantization theory and the optimal Minimum Mean Square Error(MMSE) filtering formulation to analyze, and optimize, the total end-to-end distortion. As a result, analytical expressions for the linear receive filters are obtained that minimize the total end-to-end distortion, given the quantization scheme and source(channel state) distribution. The solution to the second problem is obtained by proposing a new family of error correction codes, termed trellis coded block codes, where a trellis code and block code are concatenated in order to provide good coding gain as well as low latency and low complexity decoding. This code construction is made possible due to the existence of a uniform partitioning of linear block codes. The solution to the third problem is obtained by proposing three novel transmit precoding methods that are applicable to time-division-duplex systems, where the channel reciprocity can be exploited in designing the precoding scheme. The proposed precoding methods convert the Rayleigh fading MIMO channel into parallel Additive White Gaussian Noise(AWGN) channels with fixed gain, while satisfying an average transmit power constraint. Moreover, the receiver does not need to have knowledge of the CSI in order to decode the received data. These precoding methods are also extended to Rayleigh fading multi-user MIMO channels. Finally, all the above methods are applied to the problem of designing a low-rate, low-latency code for the noisy and fading reverse-link channel that is used for sending the CSI. Simulation results are provided to demonstrate the improvement in the forward-link data rate due to the proposed methods. Note that, although the three solutions are presented in the context of CSI feedback in MIMO communications, their development is fairly general in nature, and, consequently, the solutions are potentially applicable in other communication systems also.

Ziel dieser Arbeit ist es eine Beziehung zwischen den biophysikalischen Eigenschaften der Nervenmembran, und den ausgeführten Berechnungen und Filtereigenschaften eines tonisch feuernden Neurons, unter Einbeziehen intrinsischer Fluktuationen, herzustellen. Zu diesem Zweck werden zu erst die mikroskopischen Fluktuationen, die durch das stochastische Öffnen und Schließen der Ionenkanäle verursacht werden, zu makroskopischer Varibilität in den Zeitpunkten des Auftretens der Aktionspotentiale übersetzt, denn es sind diese Spikezeiten die in vielen sensorischen Systemen informationstragenden sind. Die Methode erlaubt es das stochastischer Verhalten komplizierter Ionenkanalstrukturen mit einer großen Zahl an Untereinheiten, in Spikezeitenvariabilität zu übersetzen. Als weiteres werden die Filtereigenschaften der Nervenzellen in der überschwelligen Dynamik, also bei Existenz eines stabilen Grenzzyklus, aus ihren Phasenantwortkurven (PAK), einer Eigenschaft des linearisierten adjungierten Flusses auf dem Grenzzyklus, in einem stöhrungstheoretischen Ansatz berechnet. Es ergibt sich, dass Charakteristika des Filter, wie beispielsweise die DC Komponente und die Eigenschaften des Filters um die Fundamentalfrequenz und ihrer Harmonien, von den Fourierkomponenten der PAK abhängen. Unter Verwendung der hergeleiteten Filter und weiterer Annahmen ist es möglich das frequenzabhängige Signal-zu-Rauschen Verhältnis zu berechnen, und damit eine untere Schranke für die Informationstransferrate eines Leitfähigkeitsmodells zu berechnen. Unter Zuhilfenahme der numerischen Kontinuierungsmethode ist es möglich die Veränderungen in der Spikevariabilität und den Filtern für jeden biophysikalischen Parameter des System zu verfolgen. Weiterhin wurde die verwendete Phasenreduktion durch eine Korrektur ergänzt, die die Radialdynamik einbezieht. Es zeigt sich, dass die Krümmung der Isochronen einen Einfluss darauf hat ob das Rauschen einen positiven oder negativen Frequenzschift hervorruft. / The goal of the thesis is to establish quantitative, analytical relations between the biophysical properties of nerve membranes and the performed neuronal computations for neurons in a tonically spiking regime and in the presence of intrinsic noise. For this purpose, two major lines of investigation are followed. Firstly, microscopic noise caused by the stochastic opening and closing of ion channels is mapped to the macroscopic spike jitter that affects neural coding. The method is generic enough to allow one to treat Markov channel models with complicated, high-dimensional state spaces and calculate from them the noise in the coding variable, i.e., the spike time. Secondly, the suprathreshold filtering properties of neurons are derived, based on the phase response curves (PRCs) by perturbing the associated Fokker-Planck equations. It turns out that key characteristics of the filter, such as the DC component of the gain and the behaviour near the fundamental frequency and its harmonics are related to the particular Fourier components of the PRC and hence the bifurcation type of the neuron. With the help of the derived filter and further approximations one is able to calculate the frequency resolved signal-to-noise ration and finally the total information transmission rate of a conductance based model. Using the method of numerical continuation it is possible to calculate the change in spike time noise level as well as the filtering properties for arbitrary changes in biophysical parameter such as varying channel densities or mean input to the cell. We extend the phase reduction to include correction terms from the amplitude dynamics that are related to the curvature of the isochrons and provide a method to identify the required amplitude sensitivities numerically. It can be shown that the curvature of the isochron has a direct consequence for the noise induced frequency shift.