Spiking Neural P Systems with Communication on Request

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Spiking Neural P Systems are Neural System models characterised by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural P systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these P systems, a speci ed number of spikes are consumed and a speci ed number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. In the present work, a novel communication strategy among neurons of Spiking Neural P Systems is proposed. In the resulting models, called Spiking Neural P Systems with Communication on Request, the spikes are requested from neighbouring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural P systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). The Spiking Neural P Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems

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

Implementation of Arithmetic Operations by SN P Systems with Communication on Request

Spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices inspired from the way neurons communicate by means of spikes. In most of the SN P systems investigated so far, the system communicates on command, and the application of evolution rules depends on the contents of a neuron. However, inspired from the parallel-cooperating grammar systems, it is natural to consider the opposite strategy: the system communicates on request, which means spikes are requested from neighboring neurons, depending on the contents of the neuron. Therefore, SN P systems with communication on request were proposed, where the spikes should be moved from a neuron to another one when the receiving neuron requests that. In this paper, we consider implementing arithmetical operations by means of SN P systems with communication on request. Specifically, adder, subtracter and multiplier are constructed by using SN P systems with communication on request

Notes About Spiking Neural P Systems

Spiking neural P systems (SN P systems, for short) are much investigated in the last years in membrane computing, but still many open problems and research topics are open in this area. Here, we first recall two such problems (both related to neural biology) from. One of them asks to build an SN P system able to store a number, and to provide it to a reader without losing it, so that the number is available for a further reading. We build here such a memory module and we discuss its extension to model/implement more general operations, specific to (simple) data bases. Then, we formulate another research issue, concerning pattern recognition in terms of SN P systems. In the context, we define a recent version of SN P systems, enlarged with rules able to request spikes from the environment; based on this version, so-called SN dP systems were recently introduced, extending to neural P systems the idea of a distributed dP automaton. Some details about such devices are also given, as a further invitation to the reader to this area of research.Junta de Andalucía P08 – TIC 0420

Logic Negation with Spiking Neural P Systems

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing.Comment: 25 pages, 1 figur

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)

Modeling Fault Propagation Paths in Power Systems: A New Framework Based on Event SNP Systems With Neurotransmitter Concentration

To reveal fault propagation paths is one of the most critical studies for the analysis of power system security; however, it is rather dif cult. This paper proposes a new framework for the fault propagation path modeling method of power systems based on membrane computing.We rst model the fault propagation paths by proposing the event spiking neural P systems (Ev-SNP systems) with neurotransmitter concentration, which can intuitively reveal the fault propagation path due to the ability of its graphics models and parallel knowledge reasoning. The neurotransmitter concentration is used to represent the probability and gravity degree of fault propagation among synapses. Then, to reduce the dimension of the Ev-SNP system and make them suitable for large-scale power systems, we propose a model reduction method for the Ev-SNP system and devise its simpli ed model by constructing single-input and single-output neurons, called reduction-SNP system (RSNP system). Moreover, we apply the RSNP system to the IEEE 14- and 118-bus systems to study their fault propagation paths. The proposed approach rst extends the SNP systems to a large-scaled application in critical infrastructures from a single element to a system-wise investigation as well as from the post-ante fault diagnosis to a new ex-ante fault propagation path prediction, and the simulation results show a new success and promising approach to the engineering domain

Modular Acquisition and Stimulation System for Timestamp-Driven Neuroscience Experiments

Dedicated systems are fundamental for neuroscience experimental protocols that require timing determinism and synchronous stimuli generation. We developed a data acquisition and stimuli generator system for neuroscience research, optimized for recording timestamps from up to 6 spiking neurons and entirely specified in a high-level Hardware Description Language (HDL). Despite the logic complexity penalty of synthesizing from such a language, it was possible to implement our design in a low-cost small reconfigurable device. Under a modular framework, we explored two different memory arbitration schemes for our system, evaluating both their logic element usage and resilience to input activity bursts. One of them was designed with a decoupled and latency insensitive approach, allowing for easier code reuse, while the other adopted a centralized scheme, constructed specifically for our application. The usage of a high-level HDL allowed straightforward and stepwise code modifications to transform one architecture into the other. The achieved modularity is very useful for rapidly prototyping novel electronic instrumentation systems tailored to scientific research.Comment: Preprint submitted to ARC 2015. Extended: 16 pages, 10 figures. The final publication is available at link.springer.co