The Experts below are selected from a list of 191274 Experts worldwide ranked by ideXlab platform
György Vaszil - One of the best experts on this subject based on the ideXlab platform.
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Developments in Language Theory - Distributed pushdown automata systems: Computational Power
Developments in Language Theory, 2003Co-Authors: Erzsébet Csuhaj-varjú, Victor Mitrana, György VaszilAbstract:We introduce distributed pushdown automata systems consisting of several pushdown automata which work in turn on the input string placed on a common one-way input tape. The work of the components is based on protocols and strategies similar to those that cooperating distributed grammar systems use. We investigate the Computational Power of these mechanisms under different protocols for activating components and two ways of accepting the input string: with empty stacks or with final states which means that all components have empty stacks or are in final states, respectively, when the input string was completely read.
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Distributed pushdown automata systems: Computational Power
Lecture Notes in Computer Science, 2003Co-Authors: Erzsébet Csuhaj-varjú, Victor Mitrana, György VaszilAbstract:We introduce distributed pushdown automata systems consisting of several pushdown automata which work in turn on the input string placed on a common one-way input tape. The work of the components is based on protocols and strategies similar to those that cooperating distributed grammar systems use. We investigate the Computational Power of these mechanisms under different protocols for activating components and two ways of accepting the input string: with empty stacks or with final states which means that all components have empty stacks or are in final states, respectively, when the input string was completely read.
Erzsébet Csuhaj-varjú - One of the best experts on this subject based on the ideXlab platform.
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Developments in Language Theory - Distributed pushdown automata systems: Computational Power
Developments in Language Theory, 2003Co-Authors: Erzsébet Csuhaj-varjú, Victor Mitrana, György VaszilAbstract:We introduce distributed pushdown automata systems consisting of several pushdown automata which work in turn on the input string placed on a common one-way input tape. The work of the components is based on protocols and strategies similar to those that cooperating distributed grammar systems use. We investigate the Computational Power of these mechanisms under different protocols for activating components and two ways of accepting the input string: with empty stacks or with final states which means that all components have empty stacks or are in final states, respectively, when the input string was completely read.
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Distributed pushdown automata systems: Computational Power
Lecture Notes in Computer Science, 2003Co-Authors: Erzsébet Csuhaj-varjú, Victor Mitrana, György VaszilAbstract:We introduce distributed pushdown automata systems consisting of several pushdown automata which work in turn on the input string placed on a common one-way input tape. The work of the components is based on protocols and strategies similar to those that cooperating distributed grammar systems use. We investigate the Computational Power of these mechanisms under different protocols for activating components and two ways of accepting the input string: with empty stacks or with final states which means that all components have empty stacks or are in final states, respectively, when the input string was completely read.
Keisuke Fujii - One of the best experts on this subject based on the ideXlab platform.
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boosting Computational Power through spatial multiplexing in quantum reservoir computing
Physical review applied, 2019Co-Authors: Keisuke Fujii, Kohei Nakajima, Makoto Negoro, Kosuke Mitarai, Masahiro KitagawaAbstract:$Q\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}m$ $r\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}s\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}r\phantom{\rule{0}{0ex}}v\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}r$ $c\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}m\phantom{\rule{0}{0ex}}p\phantom{\rule{0}{0ex}}u\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}g$ provides a scheme for exploiting the natural dynamics of quantum systems as a Computational resource. An NMR spin-ensemble system is a realistic candidate for implementing the framework, which is currently available in laboratories. Considering realistic experimental constraints, the authors propose a spatial multiplexing technique to effectively boost the platform's Computational Power. This scheme exploits disjoint dynamics of multiple, different quantum systems driven by common input streams in parallel. This allows one to prepare a huge number of qubits from individually small quantum systems, which are easy to handle in experiments.
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boosting Computational Power through spatial multiplexing in quantum reservoir computing
arXiv: Quantum Physics, 2018Co-Authors: Keisuke Fujii, Kohei Nakajima, Makoto Negoro, Kosuke Mitarai, Masahiro KitagawaAbstract:Quantum reservoir computing provides a framework for exploiting the natural dynamics of quantum systems as a Computational resource. It can implement real-time signal processing and solve temporal machine learning problems in general, which requires memory and nonlinear mapping of the recent input stream using the quantum dynamics in Computational supremacy region, where the classical simulation of the system is intractable. A nuclear magnetic resonance spin-ensemble system is one of the realistic candidates for such physical implementations, which is currently available in laboratories. In this paper, considering these realistic experimental constraints for implementing the framework, we introduce a scheme, which we call a spatial multiplexing technique, to effectively boost the Computational Power of the platform. This technique exploits disjoint dynamics, which originate from multiple different quantum systems driven by common input streams in parallel. Accordingly, unlike designing a single large quantum system to increase the number of qubits for Computational nodes, it is possible to prepare a huge number of qubits from multiple but small quantum systems, which are operationally easy to handle in laboratory experiments. We numerically demonstrate the effectiveness of the technique using several benchmark tasks and quantitatively investigate its specifications, range of validity, and limitations in detail.
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Computational Power and correlation in a quantum Computational tensor network
Physical Review A, 2012Co-Authors: Keisuke Fujii, Tomoyuki MorimaeAbstract:We investigate relationships between Computational Power and correlation in resource states for quantum Computational tensor network, which is a general framework for measurement-based quantum computation. We find that if the size of resource states is finite, not all resource states allow correct projective measurements in the correlation space, which is related to nonvanishing two-point correlations in the resource states. On the other hand, for infinite-size resource states, we can always implement correct projective measurements if the resource state can simulate arbitrary single-qubit rotations, since such a resource state exhibits exponentially decaying two-point correlations. This implies that a many-body state whose two-point correlation cannot be upper bounded by an exponentially decaying function cannot simulate arbitrary single-qubit rotations.
Wolfgang Maass - One of the best experts on this subject based on the ideXlab platform.
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NIPS - Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits
2004Co-Authors: Wolfgang Maass, Robert Legenstein, Nils BertschingerAbstract:What makes a neural microcircuit Computationally Powerful? Or more precisely, which measurable quantities could explain why one microcircuit C is better suited for a particular family of Computational tasks than another microcircuit C′? We propose in this article quantitative measures for evaluating the Computational Power and generalization capability of a neural microcircuit, and apply them to generic neural microcircuit models drawn from different distributions. We validate the proposed measures by comparing their prediction with direct evaluations of the Computational performance of these microcircuit models. This procedure is applied first to microcircuit models that differ with regard to the spatial range of synaptic connections and with regard to the scale of synaptic efficacies in the circuit, and then to microcircuit models that differ with regard to the level of background input currents and the level of noise on the membrane potential of neurons. In this case the proposed method allows us to quantify differences in the Computational Power and generalization capability of circuits in different dynamic regimes (UP- and DOWN-states) that have been demonstrated through intracellular recordings in vivo.
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On the Computational Power of circuits of spiking neurons
Journal of Computer and System Sciences, 2004Co-Authors: Wolfgang Maass, Henry MarkramAbstract:Complex real-time computations on multi-modal time-varying input streams are carried out by generic cortical microcircuits. Obstacles for the development of adequate theoretical models that could explain the seemingly universal Power of cortical microcircuits for real-time computing are the complexity and diversity of their Computational units (neurons and synapses), as well as the traditional emphasis on offline computing in almost all theoretical approaches towards neural computation. In this article, we initiate a rigorous mathematical analysis of the real-time computing capabilities of a new generation of models for neural computation, liquid state machines, that can be implemented with--in fact benefit from--diverse Computational units. Hence, realistic models for cortical microcircuits represent special instances of such liquid state machines, without any need to simplify or homogenize their diverse Computational units. We present proofs of two theorems about the potential Computational Power of such models for real-time computing, both on analog input streams and for spike trains as inputs.
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ICANN - On the Computational Power of Neural Microcircuit Models: Pointers to the Literature
Artificial Neural Networks — ICANN 2002, 2002Co-Authors: Wolfgang MaassAbstract:This paper provides references for my invited talk on the Computational Power of neural microcircuit models.
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On the Computational Power of neural microcircuit models: Pointers to the literature
Lecture Notes in Computer Science, 2002Co-Authors: Wolfgang MaassAbstract:This paper provides references for my invited talk on the Computational Power of neural microcircuit models.
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On the Computational Power of Winner-Take-All
Neural computation, 2000Co-Authors: Wolfgang MaassAbstract:This article initiates a rigorous theoretical analysis of the Computational Power of circuits that employ modules for computing winner-take-all. Computational models that involve competitive stages have so far been neglected in Computational complexity theory, although they are widely used in Computational brain models, artificial neural networks, and analog VLSI. Our theoretical analysis shows that winner-take-all is a surprisingly Powerful Computational module in comparison with threshold gates (also referred to as McCulloch-Pitts neurons) and sigmoidal gates. We prove an optimal quadratic lower bound for computing winner-take-all in any feedforward circuit consisting of threshold gates. In addition we show that arbitrary continuous functions can be approximated by circuits employing a single soft winner-take-all gate as their only nonlinear operation. Our theoretical analysis also provides answers to two basic questions raised by neurophysiologists in view of the well-known asymmetry between excitatory and inhibitory connections in cortical circuits: how much Computational Power of neural networks is lost if only positive weights are employed in weighted sums and how much adaptive capability is lost if only the positive weights are subject to plasticity.
Victor Mitrana - One of the best experts on this subject based on the ideXlab platform.
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Developments in Language Theory - Distributed pushdown automata systems: Computational Power
Developments in Language Theory, 2003Co-Authors: Erzsébet Csuhaj-varjú, Victor Mitrana, György VaszilAbstract:We introduce distributed pushdown automata systems consisting of several pushdown automata which work in turn on the input string placed on a common one-way input tape. The work of the components is based on protocols and strategies similar to those that cooperating distributed grammar systems use. We investigate the Computational Power of these mechanisms under different protocols for activating components and two ways of accepting the input string: with empty stacks or with final states which means that all components have empty stacks or are in final states, respectively, when the input string was completely read.
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Distributed pushdown automata systems: Computational Power
Lecture Notes in Computer Science, 2003Co-Authors: Erzsébet Csuhaj-varjú, Victor Mitrana, György VaszilAbstract:We introduce distributed pushdown automata systems consisting of several pushdown automata which work in turn on the input string placed on a common one-way input tape. The work of the components is based on protocols and strategies similar to those that cooperating distributed grammar systems use. We investigate the Computational Power of these mechanisms under different protocols for activating components and two ways of accepting the input string: with empty stacks or with final states which means that all components have empty stacks or are in final states, respectively, when the input string was completely read.