Coherence and Associativity as a Basis of Cognitive Architecture

In this working paper we explore a possibility of defining implementable cognitive architecture based on coherence and associativity. Drawing loosely on insights from cerebral cloning (Calvin), confabulation (Hecht-Nielsen), and coherence (Thagard), we propose a basis for a novel cognitive architecture

Cognition-based approaches for high-precision text mining

This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in. Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital and connected world, cognitive approaches enable substantial opportunities in knowledge discovery. The generation and storage of electronic text is ubiquitous, hence opportunities for improved knowledge discovery span virtually all knowledge domains. While cognition-based search offers superior approaches, challenges exist due to the need to mimic, even in the most rudimentary way, the extraordinary powers of human cognition. This research addresses these challenges in the key area of a cognition-based approach to automated concept recognition. In addition it resulted in a semantic processing system framework for use in applications in any knowledge domain. Confabulation theory was applied to the problem of automated concept recognition. This is a relatively new theory of cognition using a non-Bayesian measure, called cogency, for predicting the results of human cognition. An innovative distance measure derived from cogent confabulation and called inverse cogency, to rank order candidate concepts during the recognition process. When used with a multilayer perceptron, it improved the precision of concept recognition by 5% over published benchmarks. Additional precision improvements are anticipated. These research steps build a foundation for cognition-based, high-precision text mining. Long-term it is anticipated that this foundation enables a cognitive-based approach to automated ontology learning. Such automated ontology learning will mimic human language cognition, and will, in turn, enable the practical use of cognitive-based approaches in virtually any knowledge domain --Abstract, page iii

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Architektury kognitywne, czyli jak zbudować sztuczny umysł

Architektury kognitywne (AK) są próbą stworzenia modeli komputerowych integrujących wiedzę o działaniu umysłu. Ich zadaniem jest implementacja konkretnych schematów działania funkcji poznawczych umożliwiająca testowanie tych funkcji na szerokiej gamie zagadnień. Wiele architektur kognitywnych opracowano w celu symulacji procesu komunikacji pomiędzy człowiekiem i złożonymi maszynami (HCI, Human-Computer Interfaces), symulowania czasów reakcji oraz różnych psychofizycznych zależności. Można to do pewnego stopnia osiągnąć budując modele układu poznawczego na poziomie symbolicznym, z wiedzą w postaci reguł logicznych. Istnieją też projekty, które próbują powiązać procesy poznawcze z aktywacją modułów reprezentujących konkretne obszary mózgu, zgodnie z obserwacjami w eksperymentach z funkcjonalnym rezonansem magnetycznym (fMRI). Dużą grupę stanowią architektury oparte na podejściu logicznym, które mają na celu symulację wyższych czynności poznawczych, przede wszystkim procesów myślenia i rozumowania. Niektóre z projektów rozwoju architektur poznawczych skupiają większe grupy badawcze działające od wielu dziesięcioleci. Ogólnie architektury kognitywne podzielić można na 3 duże grupy: architektury symboliczne (oparte na funkcjonalnym rozumieniu procesów poznawczych); architektury emergentne, oparte na modelach koneksjonistycznych; oraz architektury hybrydowe, wykorzystujące zarówno modele neuronowe jak i reguły symboliczne. W ostatnich latach znacznie wzrosło zainteresowanie architekturami inspirowanymi przez neurobiologię (BICA, Brain Inspired Cognitive Architectures). Jak sklasyfikować różne architektury, jakie wyzwania należy przed nimi postawić, jak oceniać postępy w ich rozwoju, czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju czego nam brakuje do stworzenia pełnego modelu umysłu? Krytyczny przegląd istniejących architektur kognitywnych, ich ograniczeń i możliwości pozwala na sformułowanie ogólnych wniosków dotyczących kierunków ich rozwoju oraz wysunięcie własnych propozycji budowy nowej architektury

Neuromorphic Systems for Pattern Recognition and Uav Trajectory Planning

Detection and control are two essential components in an intelligent system. This thesis investigates novel techniques in both areas with a focus on the applications of handwritten text recognition and UAV flight control. Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. The difficulty is greatly increased if the detection algorithms is solely based on pattern matching without information of dynamics of handwriting trajectories. Motivated by the aforementioned challenges, this thesis first investigates the pattern recognition problem. We use offline handwritten texts recognition as a case study to explore the performance of a recurrent belief propagation model. We first develop a probabilistic inference network to post process the recognition results of deep Convolutional Neural Network (CNN) (e.g. LeNet) and collect individual characters to form words. The output of the inference network is a set of words and their probability. A series of post processing and improvement techniques are then introduced to further increase the recognition accuracy. We study the performance of proposed model through various comparisons. The results show that it significantly improves the accuracy by correcting deletion, insertion and replacement errors, which are the main sources of invalid candidate words. Deep Reinforcement Learning (DRL) has widely been applied to control the autonomous systems because it provides solutions for various complex decision-making tasks that previously could not be solved solely with deep learning. To enable autonomous Unmanned Aerial Vehicles (UAV), this thesis presents a two-level trajectory planning framework for UAVs in an indoor environment. A sequence of waypoints is selected at the higher-level, which leads the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated analytically between each pair of adjacent waypoints. The goal of trajectory generation is to maintain the stability of the UAV, and the goal of the waypoints planning is to select waypoints with the lowest control thrust throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using DRL, which learns the highly complicated and nonlinear interaction between those two levels, and the impact from the environment. Given the pre-planned trajectory, this thesis further presents an actor-critic reinforcement learning framework that realizes continuous trajectory control of the UAV through a set of desired waypoints. We construct a deep neural network and develop reinforcement learning for better trajectory tracking. In addition, Field Programmable Gate Arrays (FPGA) based hardware acceleration is designed for energy efficient real-time control. If we are to integrate the trajectory planning model onto a UAV system for real-time on-board planning, a key challenge is how to deliver required performance under strict memory and computational constraints. Techniques that compress Deep Neural Network (DNN) models attract our attention because they allow optimized neural network models to be efficiently deployed on platforms with limited energy and storage capacity. However, conventional model compression techniques prune the DNN after it is fully trained, which is very time-consuming especially when the model is trained using DRL. To overcome the limitation, we present an early phase integrated neural network weight compression system for DRL based waypoints planning. By applying pruning at an early phase, the compression of the DRL model can be realized without significant overhead in training. By tightly integrating pruning and retraining at the early phase, we achieve a higher model compression rate, reduce more memory and computing complexity, and improve the success rate compared to the original work

Neuromorphic Learning Systems for Supervised and Unsupervised Applications

The advancements in high performance computing (HPC) have enabled the large-scale implementation of neuromorphic learning models and pushed the research on computational intelligence into a new era. Those bio-inspired models are constructed on top of unified building blocks, i.e. neurons, and have revealed potentials for learning of complex information. Two major challenges remain in neuromorphic computing. Firstly, sophisticated structuring methods are needed to determine the connectivity of the neurons in order to model various problems accurately. Secondly, the models need to adapt to non-traditional architectures for improved computation speed and energy efficiency. In this thesis, we address these two problems and apply our techniques to different cognitive applications. This thesis first presents the self-structured confabulation network for anomaly detection. Among the machine learning applications, unsupervised detection of the anomalous streams is especially challenging because it requires both detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research need. We present AnRAD (Anomaly Recognition And Detection), a bio-inspired detection framework that performs probabilistic inferences. We leverage the mutual information between the features and develop a self-structuring procedure that learns a succinct confabulation network from the unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base from the data streams. Compared to several existing anomaly detection methods, the proposed approach provides competitive detection accuracy as well as the insight to reason the decision making. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementation of the recall algorithms on the graphic processing unit (GPU) and the Xeon Phi co-processor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor (GPP). The implementation enables real-time service to concurrent data streams with diversified contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle abnormal behavior detection, the framework is able to monitor up to 16000 vehicles and their interactions in real-time with a single commodity co-processor, and uses less than 0.2ms for each testing subject. While adapting our streaming anomaly detection model to mobile devices or unmanned systems, the key challenge is to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of neural models. As a follow-up to the AnRAD framework, we proposed to port the detection network to the TrueNorth architecture. Implementing inference based anomaly detection on a neurosynaptic processor is not straightforward due to hardware limitations. A design flow and the supporting component library are developed to flexibly map the learned detection networks to the neurosynaptic cores. Instead of the popular rate code, burst code is adopted in the design, which represents numerical value using the phase of a burst of spike trains. This does not only reduce the hardware complexity, but also increases the result\u27s accuracy. A Corelet library, NeoInfer-TN, is implemented for basic operations in burst code and two-phase pipelines are constructed based on the library components. The design can be configured for different tradeoffs between detection accuracy, hardware resource consumptions, throughput and energy. We evaluate the system using network intrusion detection data streams. The results show higher detection rate than some conventional approaches and real-time performance, with only 50mW power consumption. Overall, it achieves 10^8 operations per Joule. In addition to the modeling and implementation of unsupervised anomaly detection, we also investigate a supervised learning model based on neural networks and deep fragment embedding and apply it to text-image retrieval. The study aims at bridging the gap between image and natural language. It continues to improve the bidirectional retrieval performance across the modalities. Unlike existing works that target at single sentence densely describing the image objects, we elevate the topic to associating deep image representations with noisy texts that are only loosely correlated. Based on text-image fragment embedding, our model employs a sequential configuration, connects two embedding stages together. The first stage learns the relevancy of the text fragments, and the second stage uses the filtered output from the first one to improve the matching results. The model also integrates multiple convolutional neural networks (CNN) to construct the image fragments, in which rich context information such as human faces can be extracted to increase the alignment accuracy. The proposed method is evaluated with both synthetic dataset and real-world dataset collected from picture news website. The results show up to 50% ranking performance improvement over the comparison models

Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks

Neuromorphic computing refers to brain inspired algorithms and architectures. This paradigm of computing can solve complex problems which were not possible with traditional computing methods. This is because such implementations learn to identify the required features and classify them based on its training, akin to how brains function. This task involves performing computation on large quantities of data. With this inspiration, a comprehensive multi-pronged approach is employed to study and efficiently implement neuromorphic inference model using heterogeneous clusters to address the problem using traditional Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance computing (HPC) framework and optimizations are proposed for heterogeneous clusters to modularize complex neuromorphic applications in a distributed manner. To achieve best possible throughput and load balancing for such modularized architectures a set of algorithms are proposed to suggest the optimal mapping of different modules as an asynchronous pipeline to the available cluster resources while considering the complex data dependencies between stages. On the other hand, SNNs are more biologically plausible and can achieve ultra-low power implementation due to its sparse spike based communication, which is possible with emerging non-Von Neumann computing platforms. As a significant progress in this direction, spiking neuron models capable of distributed online learning are proposed. A high performance SNN simulator (SpNSim) is developed for simulation of large scale mixed neuron model networks. An accompanying digital hardware neuron RTL is also proposed for efficient real time implementation of SNNs capable of online learning. Finally, a methodology for mapping probabilistic graphical model to off-the-shelf neurosynaptic processor (IBM TrueNorth) as a stochastic SNN is presented with ultra-low power consumption