9 research outputs found
Spiking Neural P Systems with Functional Astrocytes
Spiking Neural P Systems (SN P Systems, for short) is a
developing field within the universe of P Systems. New variants arise
constantly as the study of their properties, such as computational
completeness and computational efficiency, grows. Variants frequently
incorporate new ingredients into the original model inspired by real
neurophysiological structure of the brain. A singular element present
within that structure is the astrocyte. Astrocytes, also known collectively
as astroglia, are characteristic star-shaped glial cells in the brain and
spinal cord. In this paper, a new variant of Spiking Neural P Systems
incorporating astrocytes is introduced. These astrocytes are modelled
as computing devices capable of performing function computation in a
single computation step. In order to experimentally study the action of
Spiking Neural P Systems with astrocytes, it is necessary to develop
software providing the required simulation tools. Within this trend, P–
Lingua offers a standard language for the definition of P Systems. Part
of the same software project, pLinguaCore library provides particular
implementations of parsers and simulators for the models specified in
P–Lingua. Along with the new SN P System variant with astrocytes, an
extension of the P–Lingua language allowing definition of these systems is
presented in this paper, as well as an upgrade of pLinguaCore, including
a parser and a simulator that supports the aforementioned variant.Ministerio de Ciencia e Innovación TIN2009–13192Junta de AndalucÃa P08-TIC-0420
Spiking Neural P Systems: A Short Introduction and New Normal Forms
Spiking neural P systems are a class of P systems inspired from the way
the neurons communicate with each other by means of electrical impulses (called
\spikes"). In the few years since this model was introduced, many results related
to the computing power and e ciency of these computing devices were reported.
The present paper quickly surveys the basic ideas of this research area and the basic
results, then, as typical proofs about the universality of spiking neural P systems,
we present some new normal forms for them. Speci cally, we consider a natural
restriction in the architecture of a spiking neural P system, to have neurons of a
small number of types (i.e., using a small number of sets of rules). We prove that
three types of neurons are su cient in order to generate each recursively enumerable
set of numbers as the distance between the rst two spikes emitted by the system;
the problem remains open for accepting SN P systems. The paper ends with the
complete bibliography of this domain, at the level of April 2009.Ministerio de Educación y Ciencia TIN2006-13452Junta de AndalucÃa P08-TIC-0420
Spiking Neural P Systems. Recent Results, Research Topics
After a quick introduction of spiking neural P systems (a class of P systems
inspired from the way neurons communicate by means of spikes, electrical impulses
of identical shape), and presentation of typical results (in general equivalence
with Turing machines as number computing devices, but also other issues, such as
the possibility of handling strings or infinite sequences), we present a long list of
open problems and research topics in this area, also mentioning recent attempts to
address some of them. The bibliography completes the information offered to the
reader interested in this research area.Ministerio de Educación y Ciencia TIN2006-13425Junta de AndalucÃa TIC-58
Dynamic threshold neural P systems
Pulse coupled neural networks (PCNN, for short) are models abstracting the synchronization behavior
observed experimentally for the cortical neurons in the visual cortex of a cat’s brain, and the intersecting
cortical model is a simplified version of the PCNN model. Membrane computing (MC) is a kind computation
paradigm abstracted from the structure and functioning of biological cells that provide models
working in cell-like mode, neural-like mode and tissue-like mode. Inspired from intersecting cortical
model, this paper proposes a new kind of neural-like P systems, called dynamic threshold neural P systems
(for short, DTNP systems). DTNP systems can be represented as a directed graph, where nodes are dynamic
threshold neurons while arcs denote synaptic connections of these neurons. DTNP systems provide a
kind of parallel computing models, they have two data units (feeding input unit and dynamic threshold
unit) and the neuron firing mechanism is implemented by using a dynamic threshold mechanism. The
Turing universality of DTNP systems as number accepting/generating devices is established. In addition,
an universal DTNP system having 109 neurons for computing functions is constructed.National Natural Science Foundation of China No 61472328Research Fund of Sichuan Science and Technology Project No. 2018JY0083Chunhui Project Foundation of the Education Department of China No. Z2016143Chunhui Project Foundation of the Education Department of China No. Z2016148Research Foundation of the Education Department of Sichuan province No. 17TD003
Asynchronous Spiking Neural P Systems with Multiple Channels and Symbols
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computation systems, inspired from the way that the neurons process and communicate information by means of spikes. A new variant of SNP systems, which works in asynchronous mode, asynchronous spiking neural P systems with multiple channels and symbols (ASNP-MCS systems, in short), is investigated in this paper. There are two interesting features in ASNP-MCS systems: multiple channels and multiple symbols. That is, every neuron has more than one synaptic channels to connect its subsequent neurons, and every neuron can deal with more than one type of spikes. The variant works in asynchronous mode: in every step, each neuron can be free to fire or not when its rules can be applied. The computational completeness of ASNP-MCS systems is investigated. It is proved that ASNP-MCS systems as number generating and accepting devices are Turing universal. Moreover, we obtain a small universal function computing device that is an ASNP-MCS system with 67 neurons. Specially, a new idea that can solve ``block'' problems is proposed in INPUT modules
Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity
Abstract(#br)In this paper, we create a matrix representation for spiking neural P systems with structural plasticity (SNPSP, for short), taking inspiration from existing algorithms and representations for related variants. Using our matrix representation, we provide a simulation algorithm for SNPSP systems. We prove that the algorithm correctly simulates an SNPSP system: our representation and algorithm are able to capture the syntax and semantics of SNPSP systems, e.g. plasticity rules, dynamism in the synapse set. Analyses of the time and space complexity of our algorithm show that its implementation can benefit using parallel computers. Our representation and simulation algorithm can be useful when implementing SNPSP systems and related variants with a dynamic topology, in software or..
Spiking neural P systems with astrocyte-like control
Abstract: Spiking neural P systems are computing models inspired from the way the neurons communicate by means of spikes, electrical impulses of identical shapes. In this note we consider a further important ingredient related to brain functioning, the astrocyte cells which fed neurons with nutrients, implicitly controlling their functioning. Specifically, we introduce in our models only one feature of astrocytes, formulated as a control of spikes traffic along axons. A normal form is proved (for systems without forgetting rules) and decidability issues are discussed