Journal ArticleDOI
Symbolic computation using cellular automata-based hyperdimensional computing
TLDR
This letter introduces a novel framework of reservoir computing that is capable of both connectionist machine intelligence and symbolic computation, and suggests that binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing.Abstract:
This letter introduces a novel framework of reservoir computing that is capable of both connectionist machine intelligence and symbolic computation. A cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells, and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. The proposed framework is shown to be capable of long-term memory, and it requires orders of magnitude less computation compared to echo state networks. As the focus of the letter, we suggest that binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way for concept building and symbolic processing. To demonstrate the capability of the proposed system, we make analogies directly on image data by asking, What is the automobile of air?read more
Citations
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Journal ArticleDOI
Recent advances in physical reservoir computing: A review
Gouhei Tanaka,Toshiyuki Yamane,Jean Benoit Héroux,Ryosho Nakane,Naoki Kanazawa,Seiji Takeda,Hidetoshi Numata,Daiju Nakano,Akira Hirose +8 more
TL;DR: An overview of recent advances in physical reservoir computing is provided by classifying them according to the type of the reservoir to expand its practical applications and develop next-generation machine learning systems.
Journal ArticleDOI
Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals
TL;DR: A combined method for multiclass learning and classification of various ExG biosignals such as electromyography, electroencephalography, and electrocorticography without requiring domain expert knowledge or ad hoc electrode selection process is described.
Journal ArticleDOI
Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics
TL;DR: Tradeoffs of selecting parameters of binary HD representations when applied to pattern recognition tasks are discussed and the capacity of representations of various densities is discussed.
Posted Content
A comparison of Vector Symbolic Architectures.
TL;DR: This paper provides an overview of eleven available VSA implementations and discusses their commonalities and differences in the underlying vector space and operators, and creates a taxonomy of available binding operations and shows an important ramification for non self-inverse binding operations using an example from analogical reasoning.
Proceedings ArticleDOI
Associative synthesis of finite state automata model of a controlled object with hyperdimensional computing
TL;DR: A problem of learning an evidence-based model of a plant in a distributed automation and control system is considered and the model is learned in the form a finite state automata.
References
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Distributed Representations of Words and Phrases and their Compositionality
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