Классификация пространственно-временных паттернов на основе нейроморфных сетей

This work is devoted to the problems of developing neuromorphic classifiers of spatiotemporal patterns, as well as their application in neurointerfaces. Classifiers of spatiotemporal patterns based on neural networks, support vector machines, deep neural networks, and Riemannian geometry are considered. A comparative study of these classifiers is carried out in the plane of the accuracy of multiclass recognition of electroencephalographic signals showing time-dependent bioelectrical activity in different areas of the brain during the imagination of different movements. It is shown that such classifiers can provide an accuracy of 60-80% when recognizing from two to four classes of imaginary movements. A new type of classifier based on a neuromorphic network, based on the biosimilar neurons built on the Izhikevich model, is proposed. The network processes input spike sequences and generates pulse streams of different frequencies at the outputs. The network is trained using the Supervised STDP algorithm based on labeled information containing examples of the correct recognition of the required pattern classes. The recognized pattern class is determined by the maximum frequency of the output sequence. The neuromorphic classifier showed an average classification accuracy of 90% for 4 classes of imaginary commands and a maximum of 95%. By modeling the robot control task in the virtual environment it is shown that such accuracy is sufficient for the effective use of the classifier as part of a non-invasive brain-computer interface for non-contact control of robotic devices.Эта работа посвящена проблемам разработки нейроморфных классификаторов пространственно-временных паттернов, а также их применению в нейроинтерфейсах для решения задачи управления робототехническими устройствами. Рассматриваются классификаторы пространственно-временных паттернов на основе нейронных сетей, метода опорных векторов, глубоких нейронных сетей, римановой геометрии. Проводится сравнительное исследование этих классификаторов на точность многоклассового распознавания электроэнцефалографических сигналов, показывающих зависимую от времени биоэлектрическую активность в различных зонах мозга при воображении разных движений. Показано, что такие классификаторы могут обеспечить точность 60-80% при распознавании от двух до четырех классов воображаемых движений. Предложен новый тип классификатора на основе нейроморфной сети, биоподобные нейроны которой построены на модели Ижикевича. Исходный электроэнцефалографический сигнал кодируется в импульсные потоки на основе алгоритма временного кодирования. Предложенная нейроморфная сеть обрабатывает импульсные входные последовательности и формирует на выходах импульсные потоки разной частоты. Обучение сети проводится по размеченной информации, содержащей примеры правильного распознавания нужных классов паттернов воображаемых движений с применением алгоритма Supervised STDP. Распознанный класс паттерна воображаемого движения определяется по максимальной частоте импульсного потока выходной последовательности. Нейроморфный классификатор показал среднюю точность классификации 90% для 4-х классов воображаемых двигательных команд, а максимальная точность составила 95%. Путем моделирования задачи управления роботом в виртуальной среде показано, что такая точность классификации достаточна для эффективного применения классификатора в составе неинвазивного интерфейса «мозг-компьютер» при бесконтактном управлении робототехническими устройствами

A smart campus design: data-driven and evidence-based decision support solution design

The growth and the availability of the smart devices is becoming ubiquitous today and inter-networking of these devices make up what is commonly called the Internet of Things (IoT). IoT is being used to update, enhance, simplify and automate individual lives and communities. Most of the cities in general and universities in special are adopting IoT technologies in order to create a smart sustainable living and working environments. Based on the existing literature of smart campus domain, it can be observed that there is only a small number of models as such. This study attempts to bridge the following knowledge gaps of smart campus domain. This project falls into the concept of Smart Campus and aims to design a Smart Campus solution for Staffordshire University. The primarily goal is to design a solution architecture able to collect data from remote sensor networks and analyse them with the support of data analytics and machine learning techniques for sound business decision making. The project has two stages. The first stage is the business side of the project where a business requirement study has been done to extract the exact business requirements and once this complete the second stage was the technical implementation of one or many requirements and evaluation of the solution. The scope of this paper limits to the first stage of the project. A quantitative approach was chosen by considering the nature of this study. A self-administered online questionnaire was developed around several key challenges and directed especially to the staff members, in order to identify what are the expectations of university staff in relation to thematic topics. Subsequently, business requirements under each key challenge were ranked based on MoSCoW prioritisation method. Energy management, space utilisation and occupancy, cleanliness recognition, smarter car parking, internet enabled café, network and physical security and environment (temperature) control are the key business challenges identified. Moreover, intended system qualities and specific project benefits were also identified to scope the project well

Testing simple neuron models with dendrites for sparse binary image representation

This paper deals with the problem of information representation into a form that allows to make associations, measure similarity and integrate new information with respect to previously stored. Several simple models for encoding information into sparse distributed representation are explored. These models based on the idea that information about stimuli is stored in the population, not an individual neuron, thus each neuron learns many partial features. Results show formation of a sparse representation of image data with high overlap for similar images. Each cell develops multiple receptive fields that together create a population receptive field. It was possible due to incorporation of dendritic tree into standard neuron model. Also, models were tested on a classification of handwritten digits from MNIST dataset. Results from unsupervised representation show poor accuracy compared to the state-of-the-art supervised methods, however, due to the presence of interesting properties further development of an idea should be continued.Стаття розглядає проблему представлення інформації у формі, яка дозволяє створювати асоціації, вимірювати схожість та інтегрувати нову інформацію відносно раніше збереженої. Досліджуються декілька простих моделей для кодування інформації у розріджено розподіленому представленні. Моделі ґрунтуються на ідеї, що інформація про стимули зберігається в популяції, а не в окремому нейроні, тому кожен нейрон навчається на багато часткових ознак. Результати показують формування розрідженого представлення зображення з високим перекриттям для подібних зображень. Кожна клітина формує кілька рецептивних полів, які разом утворюють популяційне рецептивне поле. Це стало можливим завдяки включенню дендритного дерева в стандартну модель нейрона. Також моделі були перевірені на здатність до класифікації рукописних цифр з набору даних MNIST. Результати для навчання без учителя мають погану точність у порівнянні з сучасними методами для навчанням з учителем, однак завдяки наявності цікавих властивостей подальший розвиток ідеї має бути продовжений

GridHTM: Grid-Based Hierarchical Temporal Memory for Anomaly Detection in Videos

The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detection in videos, but this approach has multiple problems. For example, deep learning in general has issues with noise, concept drift, explainability, and training data volumes. Additionally, anomaly detection in itself is a complex task and faces challenges such as unknownness, heterogeneity, and class imbalance. Anomaly detection using deep learning is therefore mainly constrained to generative models such as generative adversarial networks and autoencoders due to their unsupervised nature; however, even they suffer from general deep learning issues and are hard to properly train. In this paper, we explore the capabilities of the Hierarchical Temporal Memory (HTM) algorithm to perform anomaly detection in videos, as it has favorable properties such as noise tolerance and online learning which combats concept drift. We introduce a novel version of HTM, named GridHTM, which is a grid-based HTM architecture specifically for anomaly detection in complex videos such as surveillance footage. We have tested GridHTM using the VIRAT video surveillance dataset, and the subsequent evaluation results and online learning capabilities prove the great potential of using our system for real-time unsupervised anomaly detection in complex videos

Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness