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Three small universal spiking neural P systems
"... In this work we give three small spiking neural P systems. We begin by constructing a universal spiking neural P system with extended rules and only 4 neurons. This is the smallest possible number of neurons for a universal system of its kind. We prove this by showing that the set of problems solved ..."
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In this work we give three small spiking neural P systems. We begin by constructing a universal spiking neural P system with extended rules and only 4 neurons. This is the smallest possible number of neurons for a universal system of its kind. We prove this by showing that the set of problems
Small universal spiking neural P systems
"... In search for small universal computing devices of various types, we consider here the case of spiking neural P systems (SN P systems), in two versions: as devices computing functions and as devices generating sets of numbers. We start with the first case and we produce a universal spiking neural P ..."
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Cited by 23 (0 self)
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In search for small universal computing devices of various types, we consider here the case of spiking neural P systems (SN P systems), in two versions: as devices computing functions and as devices generating sets of numbers. We start with the first case and we produce a universal spiking neural
A small universal spiking neural P system
 International Workshop on Computing with Biomolecules, Vienna, Austrian Computer Society
, 2008
"... In this work we give a small extended spiking neural P system that is weakly universal. This system is significantly smaller than the smallest strongly universal spiking neural P systems. Strong universality has strict conditions regarding the encoding of input and decoding of output. Weak universal ..."
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Cited by 6 (6 self)
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In this work we give a small extended spiking neural P system that is weakly universal. This system is significantly smaller than the smallest strongly universal spiking neural P systems. Strong universality has strict conditions regarding the encoding of input and decoding of output. Weak
X.: A note on small universal spiking neural P systems
 Tenth Workshop on Membrane Computing (WMC10), Curtea de Arge¸s, Romania
, 2009
"... Summary. In the “standard ” way of simulating register machines by spiking neural P systems (in short, SN P systems), one neuron is associated with each instruction of register machine that we want to simulate. In this note, a new way is introduced for simulating register machines by SN P systems, w ..."
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Cited by 2 (1 self)
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Summary. In the “standard ” way of simulating register machines by spiking neural P systems (in short, SN P systems), one neuron is associated with each instruction of register machine that we want to simulate. In this note, a new way is introduced for simulating register machines by SN P systems
A universal spiking neural P system with 11 neurons ⋆
"... Abstract. In this work we offer a significant improvement on the previous smallest spiking neural P system. Păun and Păun [3] gave a universal spiking neural P system with 84 neurons. Subsequently, Zhang et al. [18] reduced the number of neurons used to give universality to 67. Here we give a small ..."
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Cited by 2 (2 self)
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Abstract. In this work we offer a significant improvement on the previous smallest spiking neural P system. Păun and Păun [3] gave a universal spiking neural P system with 84 neurons. Subsequently, Zhang et al. [18] reduced the number of neurons used to give universality to 67. Here we give a small
Small Byzantine Quorum Systems
 DISTRIBUTED COMPUTING
, 2001
"... In this paper we present two protocols for asynchronous Byzantine Quorum Systems (BQS) built on top of reliable channelsone for selfverifying data and the other for any data. Our protocols tolerate Byzantine failures with fewer servers than existing solutions by eliminating nonessential work in ..."
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Cited by 483 (49 self)
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In this paper we present two protocols for asynchronous Byzantine Quorum Systems (BQS) built on top of reliable channelsone for selfverifying data and the other for any data. Our protocols tolerate Byzantine failures with fewer servers than existing solutions by eliminating nonessential work
Evolving Neural Networks through Augmenting Topologies
 Evolutionary Computation
"... An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning task ..."
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Cited by 524 (113 self)
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An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixedtopology method on a challenging benchmark reinforcement learning
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
, 1989
"... The exact form of a gradientfollowing learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a precis ..."
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Cited by 529 (4 self)
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the retention of information over time periods having either fixed or indefinite length. 1 Introduction A major problem in connectionist theory is to develop learning algorithms that can tap the full computational power of neural networks. Much progress has been made with feedforward networks, and attention
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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law), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball
DISTRIBUTED SYSTEMS
, 1985
"... Growth of distributed systems has attained unstoppable momentum. If we better understood how to think about, analyze, and design distributed systems, we could direct their implementation with more confidence. ..."
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Cited by 755 (1 self)
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Growth of distributed systems has attained unstoppable momentum. If we better understood how to think about, analyze, and design distributed systems, we could direct their implementation with more confidence.
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