A neural network assembly memory model based on an optimal binary signal detection theory

A ternary/binary data coding algorithm and conditions under which Hopfield networks implement optimal convolutional and Hamming decoding algorithms has been described. Using the coding/decoding approach (an optimal Binary Signal Detection Theory, BSDT) introduced a Neural Network Assembly Memory Model (NNAMM) is built. The model provides optimal (the best) basic memory performance and demands the use of a new memory unit architecture with two-layer Hopfield network, N-channel time gate, auxiliary reference memory, and two nested feedback loops. NNAMM explicitly describes the dependence on time of a memory trace retrieval, gives a possibility of metamemory simulation, generalized knowledge representation, and distinct description of conscious and unconscious mental processes. A model of smallest inseparable part or an “atom” of consciousness is also defined. The NNAMM’s neurobiological backgrounds and its applications to solving some interdisciplinary problems are shortly discussed. BSDT could implement the “best neural code” used in nervous tissues of animals and humans.Описані тріарно-бінарний алгоритм кодування даних та умови за яких Хопфілдовські нейронні мережі реалізують для нього оптимальний конволюційний та Хемінгівський алгоритми декодування. Використовуючи запропонований підхід до кодування- декодування даних (оптимальну теорію детектування бінарних сигналів, ТДБС) будується нейросітьова ансамблева модель пам’яті (НСАМП). Ця модель забезпечує оптимальні (найкращі) основні характеристики пам’яті та вимагає використання нової архітектури елементу пам’яті що включає двошарову Хопфілдовську мережу, N-канальні часові ворота, додаткову еталонну пам’ять та дві вкладені петлі зворотнього зв’язку. НСАМП явно описує залежність від часу процессу видобування сліду пам’яті, дає можливість моделювання метапам’яті, узагальненного представлення знань та роздільного опису свідомих та підсвідомих ментальних процесів. Запропоновано також модель найменшої неділимої частки або “атому” свідомості. Коротко обговорюються нейробіологічні основи НСАМП та її застосування до вирішення деяких міждисциплінарних задач. ТДБС може реалізовувати “найкращий нейронний код” що використовується нервовими тканинами людей та тварин

Generalization by Computation Through Memory

Usually, generalization is considered as a function of learning from a set of examples. In present work on the basis of recent neural network assembly memory model (NNAMM), a biologically plausible 'grandmother' model for vision, where each separate memory unit itself can generalize, has been proposed. For such a generalization by computation through memory, analytical formulae and numerical procedure are found to calculate exactly the perfectly learned memory unit's generalization ability. The model's memory has complex hierarchical structure, can be learned from one example by a one-step process, and may be considered as a semi-representational one. A simple binary neural network for bell-shaped tuning is described

A Novel Ontology and Machine Learning Driven Hybrid Clinical Decision Support Framework for Cardiovascular Preventative Care

Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies

Debiasing reasoning:a signal detection analysis

This thesis focuses on deductive reasoning and how the belief bias effect can be reduced or ameliorated. Belief bias is a phenomenon whereby the evaluation of the logical validity of an argument is skewed by the degree to which the reasoner believes the conclusion. There has been little research examining ways of reducing such bias and whether there is some sort of effective intervention which makes people reason more on the basis of logic. Traditional analyses of this data has focussed on simple measures of accuracy, typically deducting the number of incorrect answers from the number of correct answers to give an accuracy score. However, recent theoretical developments have shown that this approach fails to separate reasoning biases and response biases. A reasoning bias, is one which affects individuals’ ability to discriminate between valid and invalid arguments, whereas a response bias is simply the individual’s tendency to give a particular answer, independent of reasoning. A Signal Detection Theory (SDT) approach is used to calculate measures of reasoning accuracy and response bias. These measures are then analysed using mixed effects models. Chapter 1 gives a general introduction to the topic, and outlines the content of subsequent chapters. In Chapter 2, I review the psychological literature around belief bias, the growth of the use of SDT models, and approaches to reducing bias. Chapter 3 covers the methodology, and includes a a thorough description of the calculation of the SDT measures, and an explanation of the mixed effects models I used to analyse these. Chapter 4 presents an experiment in which the effects of feedback on reducing belief bias is examined. In Chapter 5, the focus shifts in the direction of individual differences, and looks at the effect of different instructions given to participants, and Chapter 6 examines the effects of both feedback and specific training. Chapter 7 provides a general discussion of the implications of the previous three chapters