Denis Kleyko

Holographic Graph Neuron: A Bioinspired Architecture for Pattern Processing

In this paper, we propose a new approach to implementing hierarchical graph neuron (HGN), an architecture for memorizing patterns of generic sensor stimuli, through the use of vector symbolic architectures. The adoption of a vector symbolic representation ensures a single-layer design while retaining the existing performance characteristics of HGN. This approach significantly improves the noise resistance of the HGN architecture, and enables a linear (with respect to the number of stored entries) time search for an arbitrary subpattern.

On bidirectional transitions between localist and distributed representations : The case of common substrings search using Vector Symbolic Architecture

The contribution of this article is twofold. First, it presents an encoding approach for seamless bidirectional transitions between localist and distributed representation domains. Second, the appr ...

Exploiting bacterial properties for multi-hop nanonetworks

Molecular communication is a relatively new communication paradigm for nanomachines where the communication is realized by utilizing existing biological components found in nature. In recent years researchers have proposed using bacteria to realize molecular communication because the bacteria have the ability to swim and migrate between locations, carry DNA contents (i.e. plasmids) that could be utilized for information storage, and interact and transfer plasmids to other bacteria (one of these processes is known as bacterial conjugation). However, current proposals for bacterial nanonetworks have not considered the internal structures of the nanomachines that can facilitate the use of bacteria as an information carrier. This article presents the types and functionalities of nanomachines that can be utilized in bacterial nanonetworks. A particular focus is placed on the bacterial conjugation and its support for multihop communication between nanomachines. Simulations of the communication process have also been evaluated, to analyze the quantity of bits received as well as the delay performances. Wet lab experiments have also been conducted to validate the bacterial conjugation process. The article also discusses potential applications of bacterial nanonetworks for cancer monitoring and therapy.

Fault detection in the hyperspace: Towards intelligent automation systems

This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associative memory and vector symbolic architectures, commonly used for modeling cognitive abilities of human brain. Compared to classical methods of artificial intelligence used in the context of fault detection the proposed methodology shows an unprecedented performance, while featuring zero configuration and simple operations.

High-Dimensional Computing as a Nanoscalable Paradigm

We outline a model of computing with high-dimensional (HD) vectors—where the dimensionality is in the thousands. It is built on ideas from traditional (symbolic) computing and artificial neural nets/deep learning, and complements them with ideas from probability theory, statistics, and abstract algebra. Key properties of HD computing include a well-defined set of arithmetic operations on vectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operation, making it possible to develop efficient algorithms for large-scale real-world tasks. We present a 2-D architecture and demonstrate its functionality with examples from text analysis, pattern recognition, and biosignal processing, while achieving high levels of classification accuracy (close to or above conventional machine-learning methods), energy efficiency, and robustness with simple algorithms that learn fast. HD computing is ideally suited for 3-D nanometer circuit technology, vastly increasing circuit density and energy efficiency, and paving a way to systems capable of advanced cognitive tasks.

Dependable MAC Layer Architecture Based on Holographic Data Representation Using Hyper-Dimensional Binary Spatter Codes

In this article we propose the usage of binary spatter codes and distributed data representation for communicating loss and delay sensitive data in event-driven sensor and actuator networks. Using the proposed data representation technique along with the medium access control protocol the mission critical control information can be transmitted with assured constant delay in deployments exposing below 0 dB signal-to-noise ratio figures.

Brain-like classifier of temporal patterns

In this article we present a pattern classification system which uses Vector Symbolic Architecture (VSA) for representation, learning and subsequent classification of patterns, as a showcase we have used classification of vibration sensors measurements to vehicles types. On the quantitative side the proposed classifier requires only 1 kB of memory to classify an incoming signal against of several hundred of training samples. The classification operation into N types requires only 2*N+1 arithmetic operations this makes the proposed classifier feasible for implementation on a low-end sensor nodes. The main contribution of this article is the proposed methodology for representing temporal patterns with distributed representation and VSA-based classifier.

Fly-The-Bee: A Game Imitating Concept Learning in Bees☆

Abstract This article presents a web-based game functionally imitating a part of the cognitive behavior of a living organism. This game is a prototype implementation of an artificial online cognitive architecture based on the usage of distributed data representations and Vector Symbolic Architectures. The game demonstrates the feasibility of creating a lightweight cognitive architecture, which is capable of performing rather complex cognitive tasks. The cognitive functionality is implemented in about 100 lines of code and requires few tens of kilobytes of memory for its operation, which make the concept suitable for implementing in low-end devices such as minirobots and wireless sensors.

Modality classification of medical images with distributed representations based on cellular automata reservoir computing

Modality corresponding to medical images is a vital filter in medical image retrieval systems. This article presents the classification of modalities of medical images based on the usage of principles of hyper-dimensional computing and reservoir computing. It is demonstrated that the highest classification accuracy of the proposed method is on a par with the best classical method for the given dataset (83% vs. 84%). The major positive property of the proposed method is that it does not require any optimization routine during the training phase and naturally allows for incremental learning upon the availability of new training data.

Neural Distributed Autoassociative Memories : A Survey

Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension. The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons). Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints. Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors.