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 Andalucía P08-TIC-0420

Spiking Neural dP Systems

We bring together two topics recently introduced in membrane computing, the much investigated spiking neural P systems (in short, SN P systems), inspired from the way the neurons communicate through spikes, and the dP systems (distributed P systems, with components which "read" strings from the environment and then cooperate in accepting their concatenation). The goal is to introduce SN dP systems, and to this aim we first introduce SN P systems with the possibility to input, at their request, spikes from the environment; this is done by so-called request rules. A preliminary investigation of the obtained SN dP systems (they can also be called automata) is carried out. As expected, request rules are useful, while the distribution in terms of dP systems can handle languages which cannot be generated by usual SN P systems. We always work with extended SN P systems; the non-extended case, as well as several other natural questions remain open.Junta de Andalucía 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 Andalucía TIC-58

Languages and P Systems: Recent Developments

Languages appeared from the very beginning in membrane computing, by their length sets or directly as sets of strings. We briefly recall here this relationship, with some details about certain recent developments. In particular, we discuss the possibility to associate a control word with a computation in a P system. An improvement of a result concerning the control words of spiking neural P systems is given: regular languages can be obtained as control words of such systems with only four neurons (and with usual extended rules: no more spikes are produces than consumed). Several research topics are pointed out.Junta de Andalucía P08 – TIC 0420

On spiking neural P systems

This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants. A methodology based on the information given by the transition diagram associated with an SN P system is presented. The analysis of the diagram cycles codifies invariants formulae which enable us to establish the soundness and completeness of the system with respect to the problem it tries to resolve. We also study the universality of asynchronous and sequential SN P systems and the capability these models have to generate certain classes of languages. Further, by making a slight modification to the standard SN P systems, we introduce a new variant of SN P systems with a special I/O mode, called SN P modules, and study their computing power. It is demonstrated that, as string language acceptors and transducers, SN P modules can simulate several types of computing devices such as finite automata, a-finite transducers, and systolic trellis automata.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58

Seeking for a fingerprint: analysis of point processes in actigraphy recording

Motor activity of humans displays complex temporal fluctuations which can be characterized by scale-invariant statistics, thus documenting that structure and fluctuations of such kinetics remain similar over a broad range of time scales. Former studies on humans regularly deprived of sleep or suffering from sleep disorders predicted change in the invariant scale parameters with respect to those representative for healthy subjects. In this study we investigate the signal patterns from actigraphy recordings by means of characteristic measures of fractional point processes. We analyse spontaneous locomotor activity of healthy individuals recorded during a week of regular sleep and a week of chronic partial sleep deprivation. Behavioural symptoms of lack of sleep can be evaluated by analysing statistics of duration times during active and resting states, and alteration of behavioural organization can be assessed by analysis of power laws detected in the event count distribution, distribution of waiting times between consecutive movements and detrended fluctuation analysis of recorded time series. We claim that among different measures characterizing complexity of the actigraphy recordings and their variations implied by chronic sleep distress, the exponents characterizing slopes of survival functions in resting states are the most effective biomarkers distinguishing between healthy and sleep-deprived groups.Comment: Communicated at UPON2015, 14-17 July 2015, Barcelona. 21 pages, 11 figures; updated: figures 4-7, text revised, expanded Sec. 1,3,

Computing with cells: membrane systems - some complexity issues.

Membrane computing is a branch of natural computing which abstracts computing models from the structure and the functioning of the living cell. The main ingredients of membrane systems, called P systems, are (i) the membrane structure, which consists of a hierarchical arrangements of membranes which delimit compartments where (ii) multisets of symbols, called objects, evolve according to (iii) sets of rules which are localised and associated with compartments. By using the rules in a nondeterministic/deterministic maximally parallel manner, transitions between the system configurations can be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one membrane to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. Membrane systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future biotechnology gives way to a practical bio-realization. In this paper we survey some interesting and fundamental complexity issues such as universality vs. nonuniversality, determinism vs. nondeterminism, membrane and alphabet size hierarchies, characterizations of context-sensitive languages and other language classes and various notions of parallelism

CuSNP: Spiking Neural P Systems Simulators in CUDA

Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological neurons. CuSNP is a project involving sequential (CPU) and parallel (GPU) simulators for SN P systems. In this work, we report the following results: a P-Lingua le parser is included, for ease of use when performing simulations; extension of the matrix representation of SN P systems to include delay; comparison and analysis of our simulators by simulating two types (bitonic and generalized) of parallel sorting networks; extension of supported types of regular expressions in SN P systems. Our GPU simulator is better suited for generalized sorting as compared to bitonic sorting networks, and the GPU simulators run up to 50 faster than our CPU simulator. Finally, we discuss our experiments and provide directions for further work

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML

As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search