Showing papers by "Kamala Krithivasan published in 2012"

Computability of spiking neural p systems with anti-spikes

Abstract: Spiking neural P systems with anti-spikes (for short, SN PA systems) can encode the binary digits in a natural way using two types of objects called anti-spikes and spikes. In this paper, we use SN PA systems to perform the arithmetic operation like 2's complement, addition and subtraction of binary numbers. They are also used to simulate NAND and NOR gates.

A Variant of Distributed P Systems for Real Time Cross Layer Optimization

Abstract: Membrane computing models (also known as PS ystems) that solve opti- misation problems using genetic algorithms, ant colony optimisation, quantum-inspired evolutionary algorithm and particle swarm optimisation have been defined and are effi- ciently used in several applications. This paper describes the design of a variant of the existing Distributed P system (dP system) that is augmented with new features that enable centralised monitoring and communication with all the other components of the distributed system. This proposed model is titled Monitored Distributed P System (MDP System) and its innovative application in performing Cross Layer Optimisation in wireless adhoc networks is also presented. In the proposed MDP System each node in the network is represented by a Ps ystemthat can independently perform Cross Layer Optimisation using particle swarm optimisation. Discussions on the communica- tion complexity of the proposed model and the experimental results presented are also suggestive of the fact that the proposed Monitored Distributed P System is suitable for real time optimisation in a dynamic and distributed environment.

Modelling and simulation of sn p systems using petri nets

Abstract: Natural computing deals with the extraction of mathematical models of computation from nature, investigating their theoretical properties, and identifying the extent of their real-world applications. P systems (also called membrane systems) were introduced as parallel computational models inspired by the hierarchical structure of membranes in living organisms and the biological processes which take place in and between cells. Spiking neural P systems (for short, SN P systems) are a class of P systems inspired by the spiking activity of neurons in the brain. An SN P system is represented as a directed graph where nodes correspond to the neurons having spiking and forgetting rules. The rules involve the spikes present in the neuron in the form of occurrences of a symbol a. It is a versatile formal model of computation that can be used for designing efficient parallel algorithms for solving known computer science problems. SN P systems are used as a computing device in various ways generators, acceptors, and transducers. SN P system with anti-spikes (for short, SN PA systems) is a variant of SN P system containing two types of objects, spikes (denoted by a) and anti-spikes (denoted by a), corresponding somewhat to inhibitory impulses from neurobiology. Because of the use of two types of objects, the system can encode the binary digits in a natural way and hence can represent the formal models more efficiently and naturally than the SN P systems. The thesis investigates the computing power of spiking neural P system with anti-spikes as language generators and transducers. We show that SN PA systems as generators can generate languages that cannot be generated by the standard SN P systems. It is demonstrated that, as transducers, spiking neural P systems with anti-spikes can simulate any Boolean circuit and computing devices such as finite automata and finite transducers. We also investigate how the use of anti-spikes in spiking neural P systems affect the capability to solve the satisfiability problem in a non-deterministic way.

Representation of Spiking Neural P Systems with Anti-spikes through Petri Nets

Abstract: Spiking Neural P(SN P) system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. We propose a formal method based on Petri nets, which provides a natural and powerful framework to formalize SN P systems with anti-spikes. This enables the use of existing tools for Petri nets to study the computability and behavioural properties of SN P systems with anti-spikes.