On string languages generated by spiking neural p systems with anti-spikes
20 Nov 2011-International Journal of Foundations of Computer Science (World Scientific Publishing Company)-Vol. 22, Iss: 01, pp 15-27
TL;DR: These computing devices allow non-determinism between the rules ac → a and ac → ā, c ϵ ℕ, thus help to generate languages which cannot be generated using simple SN P systems.
Abstract: An Spiking Neural P system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. The step when system emits a spike or an anti-spike is associated with symbol 1 and 0, respectively. Here we consider these computing devices as language generators. They allow non-determinism between the rules ac → a and ac → ā, c ϵ ℕ, thus help to generate languages which cannot be generated using simple SN P systems.
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01 Jan 2018
TL;DR: This paper considers addition and subtraction operations on synapses, and proves that CSSN P systems are computationally universal as number generators, under a normal form (i.e. a simplifying set of restrictions).
Abstract: Spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing models inspired from biological spiking neurons. In this paper, we introduce a variant called SN P systems with addition/subtraction computing on synapses (CSSN P systems). CSSN P systems are inspired and motivated by the shunting inhibition of biological synapses, while incorporating ideas from dynamic graphs and networks. We consider addition and subtraction operations on synapses, and prove that CSSN P systems are computationally universal as number generators, under a normal form (i.e. a simplifying set of restrictions).
2 citations
Cites background from "On string languages generated by sp..."
...finite automata, register machines, grammars, computing numbers or strings as in [17, 18, 19, 20, 21, 22, 23, 24, 25, 26]; computing efficiency in solving hard problems, as in [27, 28]....
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28 Oct 2016
TL;DR: This work design the system by using the parallelism of the membrane system, and put all the instructions of the register machine in the same neuron, and can use less neurons to construct the system and make the simulation of instruction more concisely.
Abstract: Spiking neural P systems are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. The necessary number of neurons to construct universal spiking neural P systems is a current research hotspot. In this work, we design the system by using the parallelism of the membrane system, and put all the instructions of the register machine in the same neuron. In this way, we can use less neurons to construct the system and make the simulation of instruction more concisely. With anti-spike, in instructions execution module, we only use standard rules. A universal systems without delay having 24 neurons is constructed.
1 citations
TL;DR: In this article , the authors proposed a dual finite-field circuit based on spiking neural P systems (SN P), which can perform either the finite field addition or multiplication of two variable integers by only reconnecting their synapses dynamically.
1 citations
01 Dec 2017
TL;DR: This paper is an attempt to relax the condition of using the rules in a maximally parallel manner in the framework of spiking neural P systems with exhaustive use of rules, considering the minimal parallelism of using rules.
Abstract: This paper is an attempt to relax the condition of using the rules in a maximally parallel manner in the framework of spiking neural P systems with exhaustive use of rules. To this aim, we consider the minimal parallelism of using rules: if one rule associated with a neuron can be used, then the rule must be used at least once (but we do not care how many times). In this framework, we study the computational power of our systems as number generating devices. Weak as it might look, this minimal parallelism still leads to universality, even when we eliminate the delay between firing and spiking and the forgetting rules at the same time.
TL;DR: Adder, subtracter and multiplier are constructed by using SN P systems with communication on request, which means spikes are requested from neighboring neurons, depending on the contents of the neuron.
Abstract: Spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices inspired from the way neurons communicate by means of spikes. In most of the SN P systems investigated so far, the system communicates on command, and the application of evolution rules depends on the contents of a neuron. However, inspired from the parallel-cooperating grammar systems, it is natural to consider the opposite strategy: the system communicates on request, which means spikes are requested from neighboring neurons, depending on the contents of the neuron. Therefore, SN P systems with communication on request were proposed, where the spikes should be moved from a neuron to another one when the receiving neuron requests that. In this paper, we consider implementing arithmetical operations by means of SN P systems with communication on request. Specifically, adder, subtracter and multiplier are constructed by using SN P systems with communication on request.
Cites methods from "On string languages generated by sp..."
...Most of the classes of SN P systems obtained are computationally universal, equivalent in power to Turing machines [4, 11, 15, 31, 33, 35, 40, 42, 46, 51, 53]....
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References
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01 Jan 1979
TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
Abstract: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity. The authors present the theory in a concise and straightforward manner, with an eye out for the practical applications. Exercises at the end of each chapter, including some that have been solved, help readers confirm and enhance their understanding of the material. This book is appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
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01 Jan 1967TL;DR: In this article, the authors present an abstract theory that categorically and systematically describes what all these machines can do and what they cannot do, giving sound theoretical or practical grounds for each judgment, and the abstract theory tells us in no uncertain terms that the machines' potential range is enormous and that its theoretical limitations are of the subtlest and most elusive sort.
Abstract: From the Preface (See Front Matter for full Preface)
Man has within a single generation found himself sharing the world with a strange new species: the computers and computer-like machines. Neither history, nor philosophy, nor common sense will tell us how these machines will affect us, for they do not do "work" as did machines of the Industrial Revolution. Instead of dealing with materials or energy, we are told that they handle "control" and "information" and even "intellectual processes." There are very few individuals today who doubt that the computer and its relatives are developing rapidly in capability and complexity, and that these machines are destined to play important (though not as yet fully understood) roles in society's future. Though only some of us deal directly with computers, all of us are falling under the shadow of their ever-growing sphere of influence, and thus we all need to understand their capabilities and their limitations.
It would indeed be reassuring to have a book that categorically and systematically described what all these machines can do and what they cannot do, giving sound theoretical or practical grounds for each judgment. However, although some books have purported to do this, it cannot be done for the following reasons: a) Computer-like devices are utterly unlike anything which science has ever considered---we still lack the tools necessary to fully analyze, synthesize, or even think about them; and b) The methods discovered so far are effective in certain areas, but are developing much too rapidly to allow a useful interpretation and interpolation of results. The abstract theory---as described in this book---tells us in no uncertain terms that the machines' potential range is enormous, and that its theoretical limitations are of the subtlest and most elusive sort. There is no reason to suppose machines have any limitations not shared by man.
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01 Mar 1974
TL;DR: This book attempts to provide a comprehensive textbook for undergraduate and postgraduate mathematicians with an interest in formal languages and automata, written by Professor Ian Chiswell.
Abstract: The 80 revised papers presented together with two keynote contributions and four invited papers were carefully reviewed and sele... The study of formal languages and automata has proved to be a source of much interest and discussion amongst mathematicians in recent times. This book, written by Professor Ian Chiswell, attempts to provide a comprehensive textbook for undergraduate and postgraduate mathematicians with an interest i...
2,029 citations
Journal Article•
TL;DR: In this article, the authors introduce a class of neural-like P systems which they call spiking neural P systems (in short, SN P systems), in which the result of a computation is the time between the moments when a specified neuron spikes.
Abstract: This paper proposes a way to incorporate the idea of spiking neurons into the area of membrane computing, and to this aim we introduce a class of neural-like P systems which we call spiking neural P systems (in short, SN P systems). In these devices, the time (when the neurons fire and/or spike) plays an essential role. For instance, the result of a computation is the time between the moments when a specified neuron spikes. Seen as number computing devices, SN P systems are shown to be computationally complete (both in the generating and accepting modes, in the latter case also when restricting to deterministic systems). If the number of spikes present in the system is bounded, then the power of SN P systems falls drastically, and we get a characterization of semilinear sets. A series of research topics and open problems are formulated.
589 citations
TL;DR: This simple extension of spiking neural P systems is shown to considerably simplify the universality proofs in this area, where all rules become of the form bc → b′ or bc → lambda , where b,b′ are spikes or anti-spikes.
Abstract: Besides usual spikes employed in spiking neural P systems, we consider “anti-spikes", which participate in spiking and forgetting rules, but also annihilate spikes when meeting in the same neuron. This simple extension of spiking neural P systems is shown to considerably simplify the universality proofs in this area: all rules become of the form bc → b′ or bc → lambda , where b,b′ are spikes or anti-spikes. Therefore, the regular expressions which control the spiking are the simplest possi- ble, identifying only a singleton. A possible variation is not to produce anti-spikes in neurons, but to consider some “inhibitory synapses", which transform the spikes which pass along them into anti-spikes. Also in this case, universality is rather easy to obtain, with rules of the above simple forms.
197 citations
"On string languages generated by sp..." refers background in this paper
...SN P system with anti spikes (or SN PA system) introduced in [7], is a variant of an SN P system consisting of two types of objects, spikes (denoted as a) and anti-spikes (denoted as a)....
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