In a world dominated by temporal order, memory capable of processing, encoding and subsequently recalling ordered information is very important. Over the decades this memory, known as serial memory, has been extensively studied, and its effects are well known. Many models have also been developed, and while these models are able to reproduce the behavioural effects observed in human recall studies, they are not always implementable in a biologically plausible manner. This thesis presents the Ordinal Serial Encoding model, a model inspired by biology and designed with a broader view of general cognitive architectures in mind. This model has the advantage of simplicity, and we show how neuro-plausibility can be achieved by employing the principles of the Neural Engineering Framework in the model’s design. Additionally, we demonstrate that not only is the model able to closely mirror human performance in various recall tasks, but the behaviour of the model is itself a consequence of the underlying neural architecture.

Several decades of physiology, imaging and psychophysics research on attention has generated an enormous amount of data describing myriad forms of attentional effects. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail. However, there remains a need for neurally detailed mechanistic models of attention that connect more directly with various kinds of experimental data -- behavioural, psychophysical, neurophysiological, and neuroanatomical -- and that provide experimentally testable predictions. Research has been conducted that aims to identify neurally consistent principles that underlie selective attentional processing in cortex. The research specifically focuses on describing the functional mechanisms of attentional routing in a large-scale hierarchical model, and demonstrating the biological plausibility of the model by presenting a spiking neuron implementation that can account for a variety of attentional effects. The thesis begins by discussing several significant physiological effects of attention, and prominent brain areas involved in selective attention, which provide strong constraints for developing a model of attentional processing in cortex. Several prominent models of attention are then discussed, from which a set of common limitations in existing models is assembled that need to be addressed by the proposed model. One central limitation is that, for many existing models, it remains to be demonstrated that their computations can be plausibly performed in spiking neurons. Further, few models address attentional effects for more than a single neuron or single cortical area. And finally, few are able to account for different forms of attentional modulation in a single detailed model. These and other limitations are addressed by the Attentional Routing Circuit (ARC) proposed in this thesis. The presentation of the ARC begins with the proposal of a high-level mathematical model for selective routing in the visual hierarchy. The mathematical model is used to demonstrate that the suggested mechanisms allow for scale- and position-invariant representations of attended stimuli to be formed, and provides a functional context for interpreting detailed physiological effects. To evaluate the model's biological plausibility, the Neural Engineering Framework (NEF) is used to implement the ARC as a detailed spiking neuron model. Simulation results are then presented which demonstrate that selective routing can be performed efficiently in spiking neurons in a way that is consistent with the mathematical model. The neural circuitry for computing and applying attentional control signals in the ARC is then mapped on to neural populations in specific cortical laminae using known anatomical interlaminar and interareal connections to support the plausibility of its cortical implementation. The model is then tested for its ability to account for several forms of attentional modulation that have been reported in neurophysiological experiments. Three experiments of attention in macaque are simulated using the ARC, and for each of these experiments, the model is shown to be quantitatively consistent with measured data. Specifically, a study by Womelsdorf et al. (2008) demonstrates that spatial shifts of attention result in a shifting and shrinking of receptive fields depending on the target's position. An experiment by Treue and Martinez-Trujllo (1999) reports that attentional shifts between receptive field stimuli produce a multiplicative scaling of responses, but do not affect the neural tuning sensitivity. Finally, a study by Lee and Maunsell (2010) demonstrates that attentional shifts result in a multiplicative scaling of neural contrast-response functions that is consistent with a response-gain effect. The model accounts for each of these experimentally observed attentional effects using a single mechanism for selectively processing attended stimuli. In conclusion, it is suggested that the ARC is distinguished from previous models by providing a unifying interpretation of attentional effects at the level of single cells, neural populations, cortical areas, and over the bulk of the visual hierarchy. As well, there are several advantages of the ARC over previous models, including: (1) scalability to larger implementations without affecting the model's principles; (2) a significant increase in biological plausibility; (3) the ability to account for experimental results at multiple levels of analysis; (4) a detailed description of the model's anatomical substrate; (5) the ability to perform selective routing while preserving biological detail; and (6) generating a variety of experimentally testable predictions.

In a world dominated by temporal order, memory capable of processing, encoding and subsequently recalling ordered information is very important. Over the decades this memory, known as serial memory, has been extensively studied, and its effects are well known. Many models have also been developed, and while these models are able to reproduce the behavioural effects observed in human recall studies, they are not always implementable in a biologically plausible manner. This thesis presents the Ordinal Serial Encoding model, a model inspired by biology and designed with a broader view of general cognitive architectures in mind. This model has the advantage of simplicity, and we show how neuro-plausibility can be achieved by employing the principles of the Neural Engineering Framework in the model’s design. Additionally, we demonstrate that not only is the model able to closely mirror human performance in various recall tasks, but the behaviour of the model is itself a consequence of the underlying neural architecture.

Several decades of physiology, imaging and psychophysics research on attention has generated an enormous amount of data describing myriad forms of attentional effects. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail. However, there remains a need for neurally detailed mechanistic models of attention that connect more directly with various kinds of experimental data -- behavioural, psychophysical, neurophysiological, and neuroanatomical -- and that provide experimentally testable predictions. Research has been conducted that aims to identify neurally consistent principles that underlie selective attentional processing in cortex. The research specifically focuses on describing the functional mechanisms of attentional routing in a large-scale hierarchical model, and demonstrating the biological plausibility of the model by presenting a spiking neuron implementation that can account for a variety of attentional effects. The thesis begins by discussing several significant physiological effects of attention, and prominent brain areas involved in selective attention, which provide strong constraints for developing a model of attentional processing in cortex. Several prominent models of attention are then discussed, from which a set of common limitations in existing models is assembled that need to be addressed by the proposed model. One central limitation is that, for many existing models, it remains to be demonstrated that their computations can be plausibly performed in spiking neurons. Further, few models address attentional effects for more than a single neuron or single cortical area. And finally, few are able to account for different forms of attentional modulation in a single detailed model. These and other limitations are addressed by the Attentional Routing Circuit (ARC) proposed in this thesis. The presentation of the ARC begins with the proposal of a high-level mathematical model for selective routing in the visual hierarchy. The mathematical model is used to demonstrate that the suggested mechanisms allow for scale- and position-invariant representations of attended stimuli to be formed, and provides a functional context for interpreting detailed physiological effects. To evaluate the model's biological plausibility, the Neural Engineering Framework (NEF) is used to implement the ARC as a detailed spiking neuron model. Simulation results are then presented which demonstrate that selective routing can be performed efficiently in spiking neurons in a way that is consistent with the mathematical model. The neural circuitry for computing and applying attentional control signals in the ARC is then mapped on to neural populations in specific cortical laminae using known anatomical interlaminar and interareal connections to support the plausibility of its cortical implementation. The model is then tested for its ability to account for several forms of attentional modulation that have been reported in neurophysiological experiments. Three experiments of attention in macaque are simulated using the ARC, and for each of these experiments, the model is shown to be quantitatively consistent with measured data. Specifically, a study by Womelsdorf et al. (2008) demonstrates that spatial shifts of attention result in a shifting and shrinking of receptive fields depending on the target's position. An experiment by Treue and Martinez-Trujllo (1999) reports that attentional shifts between receptive field stimuli produce a multiplicative scaling of responses, but do not affect the neural tuning sensitivity. Finally, a study by Lee and Maunsell (2010) demonstrates that attentional shifts result in a multiplicative scaling of neural contrast-response functions that is consistent with a response-gain effect. The model accounts for each of these experimentally observed attentional effects using a single mechanism for selectively processing attended stimuli. In conclusion, it is suggested that the ARC is distinguished from previous models by providing a unifying interpretation of attentional effects at the level of single cells, neural populations, cortical areas, and over the bulk of the visual hierarchy. As well, there are several advantages of the ARC over previous models, including: (1) scalability to larger implementations without affecting the model's principles; (2) a significant increase in biological plausibility; (3) the ability to account for experimental results at multiple levels of analysis; (4) a detailed description of the model's anatomical substrate; (5) the ability to perform selective routing while preserving biological detail; and (6) generating a variety of experimentally testable predictions.

No description available.

In this thesis, we introduce several closely related stochastic models which generalize the deterministic Hodgkin-Huxley formalism to an SDE framework. We provide analytical results on the existence and uniqueness of solutions as well as the exact formulas for the moments of a simplified model, with simplifications motivated by the experiments performed by Hodgkin and Huxley in their seminal paper.For more complicated models, we provide an approach for the approximation and simulation of solutions to the corresponding SDEs, and show several realizations of the sample paths and moments of these simulations to verify qualitative behavior in this case. All code for the project is written in the Julia language and can be obtained upon request by the reader.

No description available.

Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.

Sensorische Nervenzellen kodieren Informationen über die Umwelt mittels elektrischer Impulse, sogenannte Aktionspotentiale oder Spikes. Diese werden weitergeleitet zu postsynaptischen Neuronen im zentralen Nervensystem, welche unterschiedliche Auslesestrategien verwenden. Integratorzellen summieren alle ankommenden Aktionspotentiale auf, wodurch sie die Gesamtaktivität einer präsynaptischen Population messen. Koinzidenzdetektoren hingegen, werden nur durch das synchrone Feuern der zuführenden Neuronenpopulation aktiviert. Die grundlegende Frage dieser Dissertation lautet: Welche Information eines zeitabhängigen Signals kodieren die synchronen Spikes einer Neuronenpopulation im Vergleich zu der Summe all ihrer Aktionspotentiale? Hierbei verwenden wir die Theorie stochastischer Prozesse: wir berechnen Spektralmaße, die es ermöglichen Aussagen darüber zu treffen welche Frequenzkomponenten eines Signals vorwiegend transmittiert werden. Im Gegensatz zu früheren Studien, verstehen wir unter einem synchronen Ereignis nicht zwangsläufig, dass die gesamte Population simultan feuert, sondern, dass ein minimaler Anteil („Synchronizitätsschranke") gleichzeitig aktiv ist. Unsere Analyse zeigt, dass die synchrone Populationsaktivität als ein Bandpass-Informationsfilter agieren kann: die synchronen Spikes kodieren hauptsächlich schnelle Signalanteile. Damit stellt die Selektion simultaner Neuronenaktivität ein potentielles Mittel dar um gleichzeitig anwesende, konkurrierende Signale voneinander zu trennen. Dabei hängen die genauen Charakteristika der Informationsfilterung ausschlaggebend von der Synchronizitätsschwelle ab. Insbesondere zeigt sich, dass eine Symmetrie in der Schwelle vorliegt,die die Äquivalenz der Kodierungseigenschaften von synchronem Feuern und synchronem Schweigen offenlegt. Unsere analytischen Ergebnisse testen wir mittels numerischer Simulationen und vergleichen sie mit Experimenten am schwach elektrischen Fisch. / Populations of sensory neurons encode information about the environment into electrical pulses, so called action potentials or spikes. Neurons in the brain process these pulses further by using different readout strategies. Integrator cells sum up all incoming action potentials and are thus sensitive to the overall activity of a presynaptic population. Coincidence detectors, on the other hand, are activated by the synchronous firing of the afferent population. The main question of this thesis is: What information about a common time-dependent stimulus is encoded in the synchronous spikes of a neuronal population in comparison to the sum of all spikes? We approach this question within the framework of spectral analysis of stochastic processes, which allows to assess which frequency components of a signal are predominantly encoded. Here, in contrast to earlier studies, a synchronous event does not necessarily mean that all neurons of the population fire simultaneously, but that at least a prescribed fraction ('synchrony threshold') needs to be active within a small time interval. We derive analytical expressions of the correlation statistics which are compared to numerical simulations and experiments on weakly electric fish. We show that the information transmission of the synchronous output depends highly on the synchrony threshold. We uncover a symmetry in the synchrony threshold, unveiling the similarity in the encoding capability of the common firing and the common silence of a population. Our results demonstrate that the synchronous output can act as a band-pass filter of information, i.e. it extracts predominantly fast components of a stimulus. If signals in different frequency regimes are concurrently present, the selection of synchronous firing events can thus be a tool to separate these signals.

Disertacijoje nagrinėjami nestacionarių matematinių modelių nestandartinėse srityse skaitiniai sprendimo algoritmai. Uždavinio formulavimo sritis yra šakotosios struktūros (ang. branching structures), kurių išsišakojimo taškuose apibrėžiami tvermės dėsniai. Tvermės dėsnių skaitinė analizė ir nestandartinių kraštinių sąlygų analizė skiria nagrinėjamus uždavinius nuo klasikinių aprašytų literatūroje matematinės fizikos uždavinių. Disertacijoje suformuluoti uždaviniai apima skaitinių algoritmų šakotose struktūrose su skirtingais srautų tvermės dėsniais stabilumo ir konvergavimo tyrimą, lygiagrečiųjų algoritmų sudarymą ir taikymą, skaitinių schemų uždaviniams su nelokaliomis integralinėmis sąlygomis tyrimą. Disertacijoje sprendžiami taikomieji neurono sužadinimo ir impulso relaksacijos lazerio apšviestame puslaidininkyje uždaviniai, netiesinio modelio identifikavimo uždavinys. Disertaciją sudaro įvadas, penki skyriai, rezultatų apibendrinimas, literatūros šaltinių sąrašas ir autorės publikacijų disertacijos tema sąrašas. Įvadiniame skyriuje formuluojama problema, aprašytas tyrimų objektas, darbo aktualumas, formuluojami darbo tikslai ir uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, pateikti ginamieji teiginiai ir disertacijos struktūra. Pabaigoje pristatomi pranešimai konferencijose disertacijos tema. Pirmajame skyriuje pateikta matematinių modelių nestandartinėse srityse arba su nestandartinėmis sąlygomis apžvalga. Antrajame... [toliau žr. visą tekstą] / The numerical algorithms for non-stationary mathematical models in non-standard domains are investigated in the dissertation. The problem definition domain is represented by branching structures with conjugation equations considered at the branching points. The numerical analysis of the conjugation equations and non-classical boundary conditions distinguish considered problems among the classical problems of mathematical physics presented in the literature. The scope of the dissertation covers the investigation of stability and convergence of the numerical algorithms on branching structures with different conjugation equations, the construction and implementation of parallel algorithms, the investigation of the numerical schemes for the problems with nonlocal integral conditions. The modeling of the excitation of neuron and photo-excited carrier decay in a semiconductor, also the problem of the identification of nonlinear model are considered in the dissertation. The dissertation consists of an introduction, five chapters, main conclusions, bibliography and the list of the author's publications on the topic of dissertation. Introductory chapter covers the problem formulation and the object of research, the topicality of the thesis, the aims and objectives of the dissertation, the methodology of research, scientific novelty and the practical value of the achieved results. The defended thesis and structure of the dissertation are given in this chapter. The first chapter... [to full text]