Spiking Neural P Systems with Addition/Subtraction Computing on Synapses

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)

On The Semantics of Annihilation Rules in Membrane Computing

It is well known that polarizationless recognizer P systems with active membranes, without dissolution, with division of elementary and non-elementary membranes, with antimatter and matter/antimatter annihilation rules can solve all problems in NP when the annihilation rules have (weak) priority over all the other rules. Until now, it was an open problem whether these systems can still solve all NP problems if the priority of the matter/antimatter annihilation rules is removed. In this paper we provide a negative answer to this question: we prove that the class of problems solvable by this model of P systems without priority of the matter/antimatter annihilation rules is exactly P. To the best of our knowledge, this is the rst paper in the literature of P systems where the semantics of applying the rules constitutes a frontier of tractability.Ministerio de Economía y Competitividad TIN2012-3743

Solving SAT with Antimatter in Membrane Computing

The set of NP-complete problems is split into weakly and strongly NP- complete ones. The di erence consists in the in uence of the encoding scheme of the input. In the case of weakly NP-complete problems, the intractability depends on the encoding scheme, whereas in the case of strongly NP-complete problems the problem is intractable even if all data are encoded in a unary way. The reference for strongly NP-complete problems is the Satis ability Problem (the SAT problem). In this paper, we provide a uniform family of P systems with active membranes which solves SAT { without polarizations, without dissolution, with division for elementary membranes and with matter/antimatter annihilation. To the best of our knowledge, it is the rst solution to a strongly NP-complete problem in this P system model.Ministerio de Economía y Competitividad TIN2012-3743

Recognizer P Systems with Antimatter

In this paper, we consider recognizer P systems with antimatter and the in uence of the matter/antimatter annihilation rules having weak priority over all the other rules or not. We rst provide a uniform family of P systems with active membranes which solves the strongly NP-complete problem SAT, the Satis ability Problem, without polarizations and without dissolution, yet with division for elementary membranes and with matter/antimatter annihilation rules having weak priority over all the other rules. Then we show that without this weak priority of the matter/antimatter annihilation rules over all the other rules we only obtain the complexity class PMinisterio de Economía y Competitividad TIN2012-3743

A Characterization of PSPACE with Antimatter and Membrane Creation

The use of negative information provides a new tool for exploring the limits of P systems as computational devices. In this paper we prove that the combination of antimatter and annihilation rules (based on the annihilation of physical particles and antiparticles) and membrane creation (based on autopoiesis) provides a P system model able to solve PSPACE-complete problems. Namely, we provide a uniform family of P system in such P system model which solves the satis ability problem for quanti ed Boolean formulas (QSAT). In the second part of the paper, we prove that all the decision problems which can be solved with this P system model belong to the complexity class PSPACE, so this P system model characterises PSPACE.Ministerio de Economía y Competitividad TIN2012-3743

Frontiers of Membrane Computing: Open Problems and Research Topics

This is a list of open problems and research topics collected after the Twelfth Conference on Membrane Computing, CMC 2012 (Fontainebleau, France (23 - 26 August 2011), meant initially to be a working material for Tenth Brainstorming Week on Membrane Computing, Sevilla, Spain (January 30 - February 3, 2012). The result was circulated in several versions before the brainstorming and then modified according to the discussions held in Sevilla and according to the progresses made during the meeting. In the present form, the list gives an image about key research directions currently active in membrane computing

Dendrite P Systems Toolbox: Representation, Algorithms and Simulators

Dendrite P systems (DeP systems) are a recently introduced neural-like model of computation. They provide an alternative to the more classical spiking neural (SN) P systems. In this paper, we present the first software simulator for DeP systems, and we investigate the key features of the representation of the syntax and semantics of such systems. First, the conceptual design of a simulation algorithm is discussed. This is helpful in order to shade a light on the differences with simulators for SN P systems, and also to identify potential parallelizable parts. Second, a novel simulator implemented within the PLingua simulation framework is presented. Moreover, MeCoSim, a GUI tool for abstract representation of problems based on P system models has been extended to support this model. An experimental validation of this simulator is also covered.Ministerio de Economía, Industria y Competitividad TIN2017-89842-P (MABICAP

Automatic design of deterministic and non-halting membrane systems by tuning syntactical ingredients

To solve the programmability issue of membrane computing models, the automatic design of membrane systems is a newly initiated and promising research direction. In this paper, we propose an automatic design method, Permutation Penalty Genetic Algorithm (PPGA), for a deterministic and non-halting membrane system by tuning membrane structures, initial objects and evolution rules. The main ideas of PPGA are the introduction of the permutation encoding technique for a membrane system, a penalty function evaluation approach for a candidate membrane system and a genetic algorithm for evolving a population of membrane systems toward a successful one fulfilling a given computational task. Experimental results show that PPGA can successfully accomplish the automatic design of a cell-like membrane system for computing the square of n ( n >/= 1 is a natural number) and can find the minimal membrane systems with respect to their membrane structures, alphabet, initial objects, and evolution rules for fulfilling the given task. We also provide the guidelines on how to set the parameters of PPGA

Imaging Physiological and Pathological Activity in the Brain using Electric Impedance Tomography

Electric Impedance Tomography (EIT) is a promising medical imaging technique that reconstructs the internal conductivity of an object from boundary measurements. EIT is currently being used to monitor the lung during ventilation clinically. Amongst other suggested uses for imaging it can also be used to image neuronal function. There are different ways on how EIT can image neuronal function and two of these are tested in this thesis. The overall aim of our work was to advance imaging of physiological and pathological neuronal activity using EIT and assess its potential for future clinical use. In Chapter 1, a general introduction into brain imaging techniques and EIT is given. In Chapter 2, the effect of different anaesthetics on the neuronal signal was assessed to prepare for EIT recordings under anaesthesia. In Chapter 3, we assessed the validity of two biophysical models regarding the behaviour of the impedance in response to alterations in the carrier frequency experimentally. This allowed an assessment of the ideal carrier frequency to image physiological neuronal activity. In Chapter 4, the source of the fast neural signal in EIT is discussed further. In Chapter 5, the possibility of imaging physiological neuronal activity throughout the brain is tested and its limitations are discussed. In Chapter 6, the impedance response to epileptiform activity is characterized and the potential use of EIT in imaging epileptic foci in epilepsy patients is discussed. In Chapter 7, imaging of epileptic foci in subcortical structures is tested using two different ways of imaging with EIT

Neuromorphic systems based on memristive devices - From the material science perspective to bio-inspired learning hardware

Hardware computation is facing in the present age a deep transformation of its own paradigms. Silicon based computation is reaching its limit due to the physical constraints of transistor technology. As predicted by the Moore’s law, downscaling of transistor dimensions doubled each year since the 60s, leading nowadays to the extreme of 16-nm channel width of the present state-of-the-art technology. No further improvement is possible, since laws of physics impose a different electrical behavior when lower dimensions are attempted. Multiple solutions are then envisaged, spanning the range from quantum computing to neuromorphic computing. The present dissertation wants to be a preliminary study for understanding the opportunities enabled by neuromorphic computing based on resistive switching memories. In particular, brain inspires technology and architecture of new generation processors because of its unique properties: parallel and distributed computation, superposition of processing and memory unit, low power consumption, to cite only some of them. Such features make brain particularly efficient and robust against degraded data, further than particularly suitable to process and store in memory new nformation. Despite many research projects and some commercial products are already proposing brain-like computing processors, like spiNNaker or IBM’s Bluenorth, they only mimic the brain functioning with standard Silicon technology, that is inherently serial and distinguish between processing and memory unit. Resistive switching technology on the other hand, would allow to overcome many of these issues, enabling a far better match between biological and artificial neuromorphic computation. Resistive switching are, generally speaking, Metal-Insulator-Metal structures able to change their electrical conductance as a consequence of the history of applied electric signal. In such sense, they behave exactly as synapses do in a biological neural networks. For this reason, resistive switching when modeled as memristor, i.e. memory-resistor, can act as artificial synapses and, moreover, are particularly suitable to be interfaced with artificial Silicon neurons that are designed to replicate the biological behavior when excited with electric pulses. Anyhow, from the technological standpoint, there is still no standard on the design and fabrication of resistive switching, so that multiple structure and materials are investigated. In this dissertation, it is reported an analysis of multiple resistive switching devices, based on various materials, i.e. TiO2, ZnO and HfO, and device architectures, i.e. thin film and nanostructured devices, with the scope of both characterizing and comprehending the physics behind resistive switching phenomena. Furthermore, numerical simulations of artificial spiking neural networks, embedding Silicon neurons and HfO-based resistive switching are designed and performed, in order to give a systematic analysis of the performances reached by this new kind of computing paradigm