Spiking Neural P Systems with Anti-Spikes

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 ! b0 or bc ! ¸, where b; b0 are spikes or anti-spikes. Therefore, the regular expressions which control the spiking are the simplest possible, 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.Junta de Andalucía P08 – TIC 0420

P Systems with Anti-Matter

After a short introduction to the area of membrane computing (a branch of natural computing), we introduce the concept of anti-matter in membrane computing. First we consider spiking neural P systems with anti-spikes, and then we show the power of anti-matter in cell-like P systems. As expected, the use of anti-matter objects and especially of matter/anti-matter annihilation rules, turns out to be rather powerful: computational completeness of P systems with anti-matter is obtained immediately, even without using catalysts. Finally, some open problems are formulated, too

Semantics of deductive databases with spiking neural P systems

The integration of symbolic reasoning systems based on logic and connectionist systems based on thefunctioning of living neurons is a vivid research area in computer science. In the literature, one can findmany efforts where different reasoning systems based on different logics are linked to classic artificialneural networks. In this paper, we study the relation between the semantics of reasoning systems basedon propositional logic and the connectionist model in the framework of membrane computing, namely,spiking neural P systems. We prove that the fixed point semantics of deductive databases without nega- tion can be implemented in the spiking neural P systems model and such a model can also deal withnegation if it is endowed with anti-spikes and annihilation rules

Modelling and analysis of spiking neural P systems with anti-spikes using Pnet lab

a b s t r a c t Petri Nets are promising methods for modelling and simulating biological systems. Spiking Neural P system with anti-spikes (SN PA systems) is a biologically inspired computing model that incorporates two types of objects called spikes and anti-spikes thus representing binary information in a natural way. In this paper, we propose a methodology to simulate SN PA systems using a Petri net tool called Pnet Lab. It provides a promising way for SN PA systems because of its parallel execution semantics and appropriateness to represent typical working processes of these systems. This enables us to verify system properties, system soundness and to simulate the dynamic behaviour

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)

Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms

Fuzzy Reasoning Spiking Neural P systems (FRSN P systems, for short) is a variant of Spiking Neural P systems incorporating fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge and reasoning required for fault diagnosis applications. In this sense, several FRSN P system variants have been proposed, dealing with real numbers, trapezoidal numbers, weights, etc. The model incorporating real numbers was the first introduced [13], presenting promising applications in the field of fault diagnosis of electrical systems. For this variant, a matrix-based algorithm was provided which, when executed on parallel computing platforms, fully exploits the model maximally parallel capacities. In this paper we introduce a P-Lingua framework extension to parse and simulate FRSN P systems with real numbers. Two simulators, implementing a variant of the original matrix-based simulation algorithm, are provided: a sequential one (written in Java), intended to run on traditional CPUs, and a parallel one, intended to run on CUDAenabled devices.Ministerio de Economía y Competitividad TIN2012-3743

A neural circuit for navigation inspired by C. elegans Chemotaxis

We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of concentrations. Our worm uses information regarding local temporal gradients in sodium chloride concentration to decide the instantaneous path for foraging, exploration and tracking. A key neuron pair in the C. elegans chemotaxis network is the ASEL & ASER neuron pair, which capture the gradient of concentration sensed by the worm in their graded membrane potentials. The primary sensory neurons for our network are a pair of artificial spiking neurons that function as gradient detectors whose design is adapted from a computational model of the ASE neuron pair in C. elegans. Simulations show that our worm is able to detect the set-point with approximately four times higher probability than the optimal memoryless Levy foraging model. We also show that our spiking neural network is much more efficient and noise-resilient while navigating and tracking a contour, as compared to an equivalent non-spiking network. We demonstrate that our model is extremely robust to noise and with slight modifications can be used for other practical applications such as obstacle avoidance. Our network model could also be extended for use in three-dimensional contour tracking or obstacle avoidance