Recurrence-Based Synchronization Analysis of Weakly Coupled Bursting Neurons under External ELF Fields

We investigate the response characteristics of a two-dimensional neuron model exposed to an externally applied extremely low frequency (ELF) sinusoidal electric field and the synchronization of neurons weakly coupled with gap junction. We find, by numerical simulations, that neurons can exhibit different spiking patterns, which are well observed in the structure of the recurrence plot (RP). We further study the synchronization between weakly coupled neurons in chaotic regimes under the influence of a weak ELF electric field. In general, detecting the phases of chaotic spiky signals is not easy by using standard methods. Recurrence analysis provides a reliable tool for defining phases even for noncoherent regimes or spiky signals. Recurrence-based synchronization analysis reveals that, even in the range of weak coupling, phase synchronization of the coupled neurons occurs and, by adding an ELF electric field, this synchronization increases depending on the amplitude of the externally applied ELF electric field. We further suggest a novel measure for RP-based phase synchronization analysis, which better takes into account the probabilities of recurrences.Peer Reviewe

Hierarchical Associative Memory Based on Oscillatory Neural Network

In this thesis we explore algorithms and develop architectures based on emerging nano-device technologies for cognitive computing tasks such as recognition, classification, and vision. In particular we focus on pattern matching in high dimensional vector spaces to address the nearest neighbor search problem. Recent progress in nanotechnology provides us novel nano-devices with special nonlinear response characteristics that fit cognitive tasks better than general purpose computing. We build an associative memory (AM) by weakly coupling nano-oscillators as an oscillatory neural network and design a hierarchical tree structure to organize groups of AM units. For hierarchical recognition, we first examine an architecture where image patterns are partitioned into different receptive fields and processed by individual AM units in lower levels, and then abstracted using sparse coding techniques for recognition at higher levels. A second tree structure model is developed as a more scalable AM architecture for large data sets. In this model, patterns are classified by hierarchical k-means clustering and organized in hierarchical clusters. Then the recognition process is done by comparison between the input patterns and centroids identified in the clustering process. The tree is explored in a "depth-only" manner until the closest image pattern is output. We also extend this search technique to incorporate a branch-and-bound algorithm. The models and corresponding algorithms are tested on two standard face recognition data-sets. We show that the depth-only hierarchical model is very data-set dependent and performs with 97% or 67% recognition when compared to a single large associative memory, while the branch and bound search increases time by only a factor of two compared to the depth-only search

Computing With Hybrid Material Oscillators

The evolution of computers is driven by advances not only in computer science, but also in materials science. As the post-CMOS era approaches, research is increasingly focusing on flexible and unconventional computing systems, including the study of systems that incorporate new computational paradigms into the materials, enabling the computer and the material to be the same entity. In this dissertation, we design a coupled oscillator system based on a new hybrid material that can autonomously transduce chemical, mechanical, and electrical energy. Each material unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemo-mechanical oscillations of the BZ gels deflect the piezoelectric layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these coupled units become synchronized across the network, with the mode of synchronization depending on the polarity of the piezoelectric. Taking advantage of this synchronization behavior, we demonstrate that the network of coupled BZ-PZ oscillators can perform specific computational tasks such as pattern matching in a self-organized manner, without external electrical power sources. The results of the computational modeling show that the convergence time for stable synchronization gives a distance measure between the “stored” and “input” patterns, which are encoded by the connection and phases of BZ-PZ oscillators. In addition, we demonstrate two methods to enrich the information representation in our system. One is to employ multiple BZ-PZ oscillator networks in parallel and to process information encoded in different channels. The other is to introduce capacitors into a BZ-PZ network that modify the dynamical behavior of the systems and increase the information storage. We analyze and simulate the proposed coupled oscillator systems by using linear stability analysis and phase models and explore their potential computational capabilities. Through these studies, we establish experimentally realizable design rules for creating “materials that compute”

Using Phase Response Curves to Optimize Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. April 2016. Major: Neuroscience. Advisor: Theoden Netoff. 1 computer file (PDF); vii, 190 pages.Deep brain stimulation (DBS) is a neuromodulation therapy effective at treating motor symptoms of patients with Parkinson’s disease (PD). Currently, an open-loop approach is used to set stimulus parameters, where stimulation settings are programmed by a clinician using a time intensive trial-and-error process. There is a need for a systematic approach to tuning stimulation parameters based on a patient’s physiology. An effective biomarker in the recorded neural signal is needed for this approach. It is hypothesized that DBS may work by disrupting enhanced oscillatory activity seen in PD. In this thesis I propose and provide evidence for using a simple measure, called a phase response curve, to systematically tune stimulation parameters and develop novel approaches to stimulation to suppress pathological oscillations. In this work I show that PRCs can be used to optimize stimulus frequency, waveform, and stimulus phase to disrupt a pathological oscillation in a computational model of Parkinson’s disease and/or to disrupt entrainment of single neurons in vitro. This approach has the potential to improve efficacy and reduce post-operative programming time

Biomedizinische Relevanz der quantitativen EEG-Analyse

Die vorliegende Arbeit stellt eine Zusammenfassung der bisherigen EEG-Publikationen und Ergebnisse des Verfassers dar. Das Ziel der Dissertation besteht darin, die Möglichkeiten und biomedizinische Relevanz der rechnergestützten EEG-Analyse exemplarisch aufzuzeigen. Dies erfolgt mit geeigneten Methoden der Signalverarbeitung, Nachrichtentechnik, Informationstheorie, Mustererkennung, Chaostheorie und Statistik an Hand von EEG-Daten aus verschiedenen Forschungsbereichen. Dabei werden quantitative EEG-Veränderungen, insbesondere in Abhängigkeit von dem Alter, der Vigilanz, den Schlafstadien, dem Menstruationszyklus, der Intelligenz sowie entwicklungsneurologischen Störungen, speziell beim Down Syndrom, untersucht und z. Teil hochsignifikante Ergebnisse erzielt. Besonderer Schwerpunkt wird auf die Analyse der zeitlichen Ordnung bzw. Rhythmizität des EEG-Signals mit Hilfe der Chaosanalyse gelegt, die eine signaltheoretisch bedeutende Alternative zur klassischen Spektralanalyse darstellt. Eine Störung dieser zeitlichen Ordnung kann einen prädiktiven Wert bei funktionellen Störungen des zentralen Hirnstammsystems haben. Es kann gezeigt werden, dass die diagnostische Aussagekraft des EEGs durch den Einsatz der rechnergestützten EEG-Analyse wesentlich erhöht wird. Insbesondere bei morphologischen und funktionellen Störungen des ZNS können die maschinellen EEG-Parameter als ergänzende Faktoren zur Diagnosefindung herangezogen werden. Somit tragen die Parameter der automatischen EEG-Analyse zu einer erheblich differenzierten Betrachtungsweise des EEG bei und eröffnen zusätzliche diagnostische, therapeutische und verlaufsbeurteilende Perspektiven für die klinische Elektroenzephalographie