10,394 research outputs found
Spiking Neural P Systems with Several Types of Spikes
With a motivation related to gene expression, where enzymes act in series,
somewhat similar to the train spikes traveling along the axons of neurons, we consider
an extension of spiking neural P systems, where several types of "spikes" are allowed.
The power of the obtained spiking neural P systems is investigated and the modeling of
gene expression in these terms is discussed. Some further extensions are mentioned, such
as considering a process of decay in time of the spikes.Junta de Andalucía P08 – TIC 0420
Computing with Spiking Neural P Systems: Traces and Small Universal Systems
Recently, the idea of spiking neurons and thus of computing
by spiking was incorporated into membrane computing, and so-called
spiking neural P systems (abbreviated SN P systems) were introduced.
Very shortly, in these systems neurons linked by synapses communicate
by exchanging identical signals (spikes), with the information encoded
in the distance between consecutive spikes. Several ways of using such
devices for computing were considered in a series of papers, with universality
results obtained in the case of computing numbers, both in the
generating and the accepting mode; generating, accepting, or processing
strings or infinite sequences was also proved to be of interest.
In the present paper, after a short survey of central notions and results
related to spiking neural P systems (including the case when SN P
systems are used as string generators), we contribute to this area with
two (types of) results: (i) we produce small universal spiking neural P
systems (84 neurons are sufficient in the basic definition, but this number
is decreased to 49 neurons if a slight generalization of spiking rules
is adopted), and (ii) we investigate the possibility of generating a language
by following the trace of a designated spike in its way through the
neurons.Ministerio de Educación y Ciencia TIN2005-09345-C03-0
Small Universal Spiking Neural P Systems
In search for small universal computing devices of various types, we consider
here the case of spiking neural P systems (SN P systems), in two versions: as devices
computing functions and as devices generating sets of numbers. We start with the first
case and we produce a universal spiking neural P system with 84 neurons. If a slight
generalization of the used rules is adopted, namely, we allow rules for producing simultaneously several spikes, then a considerable improvement, to 49 neurons, is obtained.
For SN P systems used as generators of sets of numbers, we find a universal system with
restricted rules having 76 neurons, and one with extended rules having 50 neurons
Fuzzy reasoning spiking neural P systems revisited: A formalization
Research interest within membrane computing is becoming increasingly interdisciplinary.In particular, one of the latest applications is fault diagnosis. The underlying mechanismwas conceived by bridging spiking neural P systems with fuzzy rule-based reasoning systems. Despite having a number of publications associated with it, this research line stilllacks a proper formalization of the foundations.National Natural Science Foundation of China No 61320106005National Natural Science Foundation of China No 6147232
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)
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
The spiking neural networks (SNNs) are considered as one of the most
promising artificial neural networks due to their energy efficient computing
capability. Recently, conversion of a trained deep neural network to an SNN has
improved the accuracy of deep SNNs. However, most of the previous studies have
not achieved satisfactory results in terms of inference speed and energy
efficiency. In this paper, we propose a fast and energy-efficient information
transmission method with burst spikes and hybrid neural coding scheme in deep
SNNs. Our experimental results showed the proposed methods can improve
inference energy efficiency and shorten the latency.Comment: Accepted to DAC 201
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