Analysing and enhancing the performance of associative memory architectures

This thesis investigates the way in which information about the structure of a set of training data with 'natural' characteristics may be used to positively influence the design of associative memory neural network models of the Hopfield type. This is done with a view to reducing the level of connectivity in models of this type. There are three strands to this work. Firstly, an empirical evaluation of the implementation of existing theory is given. Secondly, a number of existing theories are combined to produce novel network models and training regimes. Thirdly, new strategies for constructing and training associative memories based on knowledge of the structure of the training data are proposed. The first conclusion of this work is that, under certain circumstances, performance benefits may be gained by establishing the connectivity in a non-random fashion, guided by the knowledge gained from the structure of the training data. These performance improvements exist in relation to networks in which sparse connectivity is established in a purely random manner. This dilution occurs prior to the training of the network. Secondly, it is verified that, as predicted by existing theory, targeted post-training dilution of network connectivity provides greater performance when compared with networks in which connections are removed at random. Finally, an existing tool for the analysis of the attractor performance of neural networks of this type has been modified and improved. Furthermore, a novel, comprehensive performance analysis tool is proposed

생쥐 전대상피질의 가려움 및 고통 신경 네트워크에 대한 시스템적 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 협동과정 뇌과학전공, 2023. 8. 강봉균.고통 및 가려움의 회로 및 메커니즘은 주로 수용체 및 신경절에서 주로 연구되어 밀접한 관련이 있음이 알려져 있으나, 뇌 내부의 정보처리 과정에서 이 두 자극이 어떻게 구분되고 서로 영향을 주는지에 대해서는 상대적으로 잘 알려져 있지 않다. 전대상피질은 뇌 영역의 하나로 고통과 가려움 감각을 처리하는 것으로 알려져 있어, 해당 영역에서 이 두 자극을 구분하고자 하였다. 이 연구에서는 약물에 의해 유발된 고통 및 가려움 상태에서 전대상피질에 대한 1-photon 칼슘 이미지를 관찰하였다. 고통과 가려움을 표지하는 뉴런 집단들의 시간 의존성을 먼저 확인하였고, 파워 스펙트럼 밀도 분석을 기반으로 신경 발화 패턴을 분석한 결과는 고통과 가려움 자극에 따라 전대상피질이 뚜렷한 발화 패턴을 가짐을 보였다. 네트워크 분석 결과는 전대상피질에서 처리되는 고통 혹은 가려움 반응 동안 시간 의존적인 기능적 허브의 존재를 암시한다. 본 연구는 고통과 가려움 처리를 나타내는 두 개의 뉴런 집단의 존재를 밝혔다.The circuits and mechanisms of pain and itch have been studied primarily in receptors and ganglia in the peripheral nervous system and are closely related, but relatively little is known about how these two stimuli are differentiated and influence each other in the central nervous system. The anterior cingulate cortex is a brain region known to process pain and itch sensations, but their interactions are not well understood. In this study, I looked at 1-photon calcium imaging of the anterior cingulate cortex during drug-induced pain and itch. I first identified the time dependence of neuronal populations labeling pain and itch, and analyzed neural firing patterns based on power spectral density analysis, which showed that the anterior cingulate cortex had distinct firing patterns in response to pain and itch stimuli. The network analysis results suggest the existence of time-dependent functional hubs during pain or itch responses processed in the anterior cingulate cortex. The present study revealed the existence of two neuronal populations representing pain and itch processing.Chapter 1. Introduction 1 ............................................................................. Chapter 2. Calcium image shows pain/itch has its own population 12 ... Chapter 3. Itch and pain signal distinguished by using ML classifier 21 Chapter 4. Itch and pain have their own network structure 27 ............... Chapter 5. Behavior data shows scratching has distinct response 33 ...... Chapter 6. Conclusion 37 ............................................................................. References 41 ................................................................................................. Abstract in Korean ..................................................................................47박

Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making

Integrating the key approaches of neural networks

The thesis is written in chapter form. Chapter 1 describes some of the history of neural networks and its place in the field of artificial intelligence. It indicates the biological basis from which neural network approximation are made. Chapter 2 describes the properties of neural networks and their uses. It introduces the concepts of training and learning. Chapters 3, 4, 5 and 6 show the perceptron and adaline in feedforward and recurrent networks particular reference is made to regression substitution by "group method data handling. Networks are chosen that explain the application of neural networks in classification, association, optimization and self organization. Chapter 7 addresses the subject of practical inputs to neural networks. Chapter 8 reviews some interesting recent developments. Chapter 9 reviews some ideas on the future technology for neural networks. Chapter 10 gives a listing of some neural network types and their uses. Appendix A gives some of the ideas used in portfolio selection for the Johannesburg Stock Exchange.ComputingM. Sc. (Operations Research

An optimal nephelometric model design method for particle characterisation

Scattering nephelometry is a particle characterisation method applicable to fluid suspensions containing impurities. Solutions derived by the method feature particle classification by size (diameter), volume or texture as well as continuous on-line and in-situ monitoring, The replacement of turbidimeters with nephelometers in many existing turbidity applications could result in suppression of side effects caused by limitations and uncontrolled parameter drifts and satisfaction of problem-defined constraints at virtually no change in implementation cost. A major issue of nephelometric model design is the selection of a mathematical tool suitable for the modelling of the data analysis system. [Continues.

Network science and the effects of music on the human brain

Most people choose to listen to music that they prefer or like such as classical, country or rock. Previous research has focused on how different characteristics of music (i.e., classical versus country) affect the brain. Yet, when listening to preferred music regardless of the type--people report they often experience personal thoughts and memories. To date, understanding how this occurs in the brain has remained elusive. Using network science methods, I evaluated differences in functional brain connectivity when individuals listened to complete songs. Here the results reveal that a circuit important for internally focused thoughts, known as the default mode network, was most connected when listening to preferred music. The results also reveal that listening to a favorite song alters the connectivity between auditory brain areas and the hippocampus, a region responsible for memory and social emotion consolidation. Given that musical preferences are uniquely individualized phenomena and that music can vary in acoustic complexity and the presence or absence of lyrics, the consistency of these results was contrary to previous neuroscientific understanding. These findings may explain why comparable emotional and mental states can be experienced by people listening to music that differs as widely as Beethoven and Eminem. The neurobiological and neurorehabilitation implications of these results are discussed